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Madadi Y, Abu-Serhan H, Yousefi S. Domain Adaptation-Based Deep Learning Model for Forecasting and Diagnosis of Glaucoma Disease. Biomed Signal Process Control 2024; 92:106061. [PMID: 38463435 PMCID: PMC10922017 DOI: 10.1016/j.bspc.2024.106061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
The main factor causing irreversible blindness is glaucoma. Early detection greatly reduces the risk of further vision loss. To address this problem, we developed a domain adaptation-based deep learning model called Glaucoma Domain Adaptation (GDA) based on 66,742 fundus photographs collected from 3272 eyes of 1636 subjects. GDA learns domain-invariant and domain-specific representations to extract both general and specific features. We also developed a progressive weighting mechanism to accurately transfer the source domain knowledge while mitigating the transfer of negative knowledge from the source to the target domain. We employed low-rank coding for aligning the source and target distributions. We trained GDA based on three different scenarios including eyes annotated as glaucoma due to 1) optic disc abnormalities regardless of visual field abnormalities, 2) optic disc or visual field abnormalities except ones that are glaucoma due to both optic disc and visual field abnormalities at the same time, and 3) visual field abnormalities regardless of optic disc abnormalities We then evaluate the generalizability of GDA based on two independent datasets. The AUCs of GDA in forecasting glaucoma based on the first, second, and third scenarios were 0.90, 0.88, and 0.80 and the Accuracies were 0.82, 0.78, and 0.72, respectively. The AUCs of GDA in diagnosing glaucoma based on the first, second, and third scenarios were 0.98, 0.96, and 0.93 and the Accuracies were 0.93, 0.91, and 0.88, respectively. The proposed GDA model achieved high performance and generalizability for forecasting and diagnosis of glaucoma disease from fundus photographs. GDA may augment glaucoma research and clinical practice in identifying patients with glaucoma and forecasting those who may develop glaucoma thus preventing future vision loss.
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Affiliation(s)
- Yeganeh Madadi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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Gabaldi CQ, Cypriano AS, Pedrotti CHS, Malheiro DT, Laselva CR, Cendoroglo M, Teich VD. Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data? Einstein (Sao Paulo) 2024; 22:eAO0328. [PMID: 38477720 PMCID: PMC10948090 DOI: 10.31744/einstein_journal/2024ao0328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/14/2023] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Gabaldi et al. utilized telemedicine data, web search trends, hospitalized patient characteristics, and resource usage data to estimate bed occupancy during the COVID-19 pandemic. The results showcase the potential of data-driven strategies to enhance resource allocation decisions for an effective pandemic response. OBJECTIVE To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. METHODS Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. RESULTS The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. CONCLUSION The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources. BACKGROUND Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. BACKGROUND Telemedicine calls from COVID-19 patients correlated with the number of patients hospitalized in the next 8 days.
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Affiliation(s)
- Caio Querino Gabaldi
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
| | - Adriana Serra Cypriano
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
| | | | - Daniel Tavares Malheiro
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
| | - Claudia Regina Laselva
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
| | - Miguel Cendoroglo
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
| | - Vanessa Damazio Teich
- Hospital Israelita Albert EinsteinSão PauloSPBrazilHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
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Ambrutis A, Povilaitis M. Composite rating method: Application to European basketball leagues. J Sports Sci 2024:1-14. [PMID: 38446425 DOI: 10.1080/02640414.2024.2326275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
This paper introduces the Composite Rating Method (CRM), a novel approach for the integrated evaluation of basketball player and team performances across multiple leagues. Utilizing data from Euroleague, EuroCup, and Basketball Champions League, the presented method provides comprehensive and accurate rankings, including accounting for actions not included in personal statistics. Drawing inspiration from established methodologies such as ELO, PER, Offensive and Defensive ratings, CRM offers a balanced assessment of player and team capabilities. The paper delineates the data collection and preprocessing procedures, details the algorithmic framework of CRM, and showcases its predictive capacity. By presenting a well-rounded approach to ranking, this paper aims to contribute to the advancement of performance evaluation methods in basketball and sports in general.
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Affiliation(s)
- Andrius Ambrutis
- Lithuanian Energy Institute, Laboratory of Nuclear Installation Safety, kaunas, Lithuania
| | - Mantas Povilaitis
- Lithuanian Energy Institute, Laboratory of Nuclear Installation Safety, kaunas, Lithuania
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Kiryluk HD, Beard CB, Holcomb KM. The use of environmental data in descriptive and predictive models of vector-borne disease in North America. J Med Entomol 2024:tjae031. [PMID: 38431876 DOI: 10.1093/jme/tjae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/25/2024] [Accepted: 02/14/2024] [Indexed: 03/05/2024]
Abstract
Vector-borne disease incidence and burden are on the rise. Weather events and climate patterns are known to influence vector populations and disease distribution and incidence. Changes in weather trends and climatic factors can shift seasonal vector activity and host behavior, thus altering pathogen distribution and introducing diseases to new geographic regions. With the upward trend in global temperature, changes in the incidence and distribution of disease vectors possibly linked to climate change have been documented. Forecasting and modeling efforts are valuable for incorporating climate into predicting changes in vector and vector-borne disease distribution. These predictions serve to optimize disease outbreak preparedness and response. The purpose of this scoping review was to describe the use of climate data in vector-borne disease prediction in North America between 2000 and 2022. The most investigated diseases were West Nile virus infection, Lyme disease, and dengue. The uneven geographical distribution of publications could suggest regional differences in the availability of surveillance data required for vector-borne disease predictions and forecasts across the United States, Canada, and Mexico. Studies incorporated environmental data from ground-based sources, satellite data, previously existing data, and field-collected data. While environmental data such as meteorological and topographic factors were well-represented, further research is warranted to ascertain if relationships with less common variables, such as oceanographic characteristics and drought, hold among various vector populations and throughout wider geographical areas. This review provides a catalogue of recently used climatic data that can inform future assessments of the value of such data in vector-borne disease models.
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Affiliation(s)
- Hanna D Kiryluk
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521, USA
- Colorado School of Public Health, Colorado State University, Sage Hall, Campus Delivery 1612, Fort Collins, CO 80523, USA
- College of Veterinary Medicine and Biomedical Sciences, Colorado State University, 1601 Campus Delivery, Fort Collins, CO 80523, USA
| | - Charles B Beard
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521, USA
| | - Karen M Holcomb
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521, USA
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Klinkenberg D, Backer J, de Keizer N, Wallinga J. Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021. Euro Surveill 2024; 29. [PMID: 38456214 DOI: 10.2807/1560-7917.es.2024.29.10.2300336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024] Open
Abstract
BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.
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Affiliation(s)
- Don Klinkenberg
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Jantien Backer
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Nicolette de Keizer
- Department of Medical Informatics, Amsterdam UMC, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- National Intensive Care Evaluation (NICE) Foundation, Amsterdam, The Netherlands
| | - Jacco Wallinga
- Leiden University Medical Centre, Leiden, The Netherlands
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
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Heggerud CM, Hastings A. A model free method of predicting transient dynamics in anaerobic digestion. J R Soc Interface 2024; 21:20240059. [PMID: 38531409 DOI: 10.1098/rsif.2024.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024] Open
Abstract
Transient dynamics pose unique challenges when dealing with predictions and management of ecological systems yet little headway has been made on understanding when an ecological system might be in a transient state. As a start we consider a specific model, here focusing on a canonical model for anaerobic digestion. Through a series of simplifications, we analyse the potential of the model for transient dynamics, and the driving mechanisms. Using a stochastic analogue of this model, we create synthetic ecological data. Thus, combining our understanding of the deterministic transient dynamics with the use of empirical dynamical modelling, we propose several new metrics to indicate when the synthetic time series is leaving a transient state.
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Affiliation(s)
- Christopher M Heggerud
- Department of Environmental Science and Policy, University of California Davis, Davis, CA, USA
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California Davis, Davis, CA, USA
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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de Oliveira Aparecido LE, de Lima RF, Torsoni GB, Lorençone JA, Lorençone PA, de Souza Rolim G. Climate and disease: tackling coffee brown-eye spot with advanced forecasting models. J Sci Food Agric 2024. [PMID: 38349004 DOI: 10.1002/jsfa.13379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. RESULTS These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). CONCLUSION The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.
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Affiliation(s)
| | | | | | | | | | - Glauco de Souza Rolim
- Faculdade de Ciências Agrárias e Veterinárias-Câmpus de Jaboticabal-Unesp, Jaboticabal, Brazil
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Khan N, Raza MA, Mirjat NH, Balouch N, Abbas G, Yousef A, Touti E. Corrigendum: Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease. Front Artif Intell 2024; 7:1373254. [PMID: 38371347 PMCID: PMC10869599 DOI: 10.3389/frai.2024.1373254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/20/2024] Open
Abstract
[This corrects the article DOI: 10.3389/frai.2023.1339988.].
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Affiliation(s)
- Nitasha Khan
- Department of Electrical Engineering, Nazeer Hussain University, Karachi, Pakistan
| | - Muhammad Amir Raza
- Department of Electrical Engineering, Mehran University of Engineering and Technology, Khairpur Mirs, Sindh, Pakistan
| | - Nayyar Hussain Mirjat
- Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan
| | - Neelam Balouch
- Department of Zoology, Shah Abdul Latif University Khairpur Mirs, Khairpur Mirs, Pakistan
| | - Ghulam Abbas
- School of Electrical Engineering, Southeast University, Nanjing, China
| | - Amr Yousef
- Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia
- Engineering Mathematics Department, Alexandria University, Alexandria, Egypt
| | - Ezzeddine Touti
- Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia
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Isik H, Tasdemir S, Taspinar YS, Kursun R, Cinar I, Yasar A, Yasin ET, Koklu M. Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models. Food Sci Nutr 2024; 12:786-803. [PMID: 38370035 PMCID: PMC10867492 DOI: 10.1002/fsn3.3783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 02/20/2024] Open
Abstract
The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short-Term Memory) and BiLSTM (Bi-directional Long Short-Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.
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Affiliation(s)
- Hakan Isik
- Department of Electric‐Electronic EngineeringSelcuk UniversityKonyaTurkey
| | - Sakir Tasdemir
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
| | | | - Ramazan Kursun
- Guneysinir Vocational SchoolSelcuk UniversityKonyaTurkey
| | - Ilkay Cinar
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
| | - Ali Yasar
- Department of Mechatronic EngineeringSelcuk UniversityKonyaTurkey
| | | | - Murat Koklu
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
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Horiguchi D, Shin S, Pepino JA, Peterson JT, Kehoe IE, Goldstein JN, Lee J, Kwon BK, Hahn JO, Reisner AT. Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis. J Intensive Care Med 2024:8850666241226893. [PMID: 38282376 DOI: 10.1177/08850666241226893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.
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Affiliation(s)
- Daisuke Horiguchi
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Nihon Kohden Innovation Center, LLC, Cambridge, MA, USA
| | - Sungtae Shin
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Jeremy A Pepino
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey T Peterson
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Iain E Kehoe
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua N Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jarone Lee
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston MA, USA
| | - Brian K Kwon
- Department of Orthopaedics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Andrew T Reisner
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
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Zacarias H, Marques JAL, Felizardo V, Pourvahab M, Garcia NM. ECG Forecasting System Based on Long Short-Term Memory. Bioengineering (Basel) 2024; 11:89. [PMID: 38247966 PMCID: PMC10813352 DOI: 10.3390/bioengineering11010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals' nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal's structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model's accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.
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Affiliation(s)
- Henriques Zacarias
- Faculdade de Ciências de Saúde, Universidade da Beira Interior, 6201-001 Covilha, Portugal
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Instituto Politécnico da Huíla, Universidade Mandume Ya Ndemufayo, Lubango 1049-001, Angola
| | | | - Virginie Felizardo
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Departamento de Informática, Universidade da Beira Interior, 6201-001 Covilha, Portugal;
| | - Mehran Pourvahab
- Departamento de Informática, Universidade da Beira Interior, 6201-001 Covilha, Portugal;
| | - Nuno M. Garcia
- Instituto de Telecomunicacoes, 6201-001 Lisboa, Portugal; (V.F.); (N.M.G.)
- Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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Chu JY, Voelkel JG, Stagnaro MN, Kang S, Druckman JN, Rand DG, Willer R. Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes. Proc Natl Acad Sci U S A 2024; 121:e2307008121. [PMID: 38215187 PMCID: PMC10801850 DOI: 10.1073/pnas.2307008121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 11/08/2023] [Indexed: 01/14/2024] Open
Abstract
Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.
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Affiliation(s)
- James Y. Chu
- Department of Sociology, Columbia University, New York, NY10027
| | - Jan G. Voelkel
- Department of Sociology, Stanford University, Stanford, CA94305
| | - Michael N. Stagnaro
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Suji Kang
- Perry World House, University of Pennsylvania, Philadelphia, PA19104
| | - James N. Druckman
- Department of Political Science, University of Rochester, Rochester, NY14627
| | - David G. Rand
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Robb Willer
- Department of Sociology, Stanford University, Stanford, CA94305
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14
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Bekbolatova M, Mayer J, Ong CW, Toma M. Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives. Healthcare (Basel) 2024; 12:125. [PMID: 38255014 PMCID: PMC10815906 DOI: 10.3390/healthcare12020125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.
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Affiliation(s)
- Molly Bekbolatova
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Jonathan Mayer
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
| | - Chi Wei Ong
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore
| | - Milan Toma
- Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA; (M.B.); (J.M.)
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15
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Khan N, Raza MA, Mirjat NH, Balouch N, Abbas G, Yousef A, Touti E. Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease. Front Artif Intell 2024; 6:1339988. [PMID: 38259821 PMCID: PMC10801895 DOI: 10.3389/frai.2023.1339988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/08/2023] [Indexed: 01/24/2024] Open
Abstract
In today's modern era, chronic kidney disease stands as a significantly grave ailment that detrimentally impacts human life. This issue is progressively escalating in both developed and developing nations. Precise and timely identification of chronic kidney disease is imperative for the prevention and management of kidney failure. Historical methods of diagnosing chronic kidney disease have often been deemed unreliable on several fronts. To distinguish between healthy individuals and those afflicted by chronic kidney disease, dependable and effective non-invasive techniques such as machine learning models have been adopted. In our ongoing research, we employ various machine learning models, encompassing logistic regression, random forest, decision tree, k-nearest neighbor, and support vector machine utilizing four kernel functions (linear, Laplacian, Bessel, and radial basis kernels), to forecast chronic kidney disease. The dataset used constitutes records from a case-control study involving chronic kidney disease patients in district Buner, Khyber Pakhtunkhwa, Pakistan. For comparative evaluation of the models in terms of classification and accuracy, diverse performance metrics, including accuracy, Brier score, sensitivity, Youden's index, and F1 score, were computed.
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Affiliation(s)
- Nitasha Khan
- Department of Electrical Engineering, Nazeer Hussain University, Karachi, Pakistan
| | - Muhammad Amir Raza
- Department of Electrical Engineering, Mehran University of Engineering and Technology, Khairpur Mirs, Sindh, Pakistan
| | - Nayyar Hussain Mirjat
- Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan
| | - Neelam Balouch
- Department of Zoology, Shah Abdul Latif University Khairpur Mirs, Khairpur Mirs, Pakistan
| | - Ghulam Abbas
- School of Electrical Engineering, Southeast University, Nanjing, China
| | - Amr Yousef
- Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia
- Engineering Mathematics Department, Alexandria University, Alexandria, Egypt
| | - Ezzeddine Touti
- Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia
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16
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Clare JDJ, de Valpine P, Moanga DA, Tingley MW, Beissinger SR. A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change. Glob Chang Biol 2024; 30:e17019. [PMID: 37987241 DOI: 10.1111/gcb.17019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/04/2023] [Accepted: 10/07/2023] [Indexed: 11/22/2023]
Abstract
Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.
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Affiliation(s)
- John D J Clare
- Museum of Vertebrate Zoology, University of California-Berkeley, Berkeley, California, USA
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
| | - Perry de Valpine
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
| | - Diana A Moanga
- Department of Earth System Science, Stanford University, Palo Alto, California, USA
| | - Morgan W Tingley
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California, USA
| | - Steven R Beissinger
- Museum of Vertebrate Zoology, University of California-Berkeley, Berkeley, California, USA
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA
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17
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Abushanab D, Al-Badriyeh D, Marquina C, Liew D, Al-Zaidan M, Ghaith Al-Kuwari M, Abdulmajeed J, Ademi Z. Societal health and economic burden of cardiovascular diseases in the population with type 2 diabetes in Qatar. A 10-year forecasting model. Diabetes Obes Metab 2024; 26:148-159. [PMID: 37845584 DOI: 10.1111/dom.15299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/04/2023] [Accepted: 09/13/2023] [Indexed: 10/18/2023]
Abstract
AIMS To predict the future health and economic burden of cardiovascular disease (CVD) in type 2 diabetes (T2D) in Qatar. MATERIALS AND METHODS A dynamic multistate model was designed to simulate the progression of fatal and non-fatal CVD events among people with T2D in Qatar aged 40-79 years. First CVD events [i.e. myocardial infarction (MI) and stroke] were calculated via the 2013 Pooled Cohort Equation, while recurrent CVD events were sourced from the REACH registry. Key model outcomes were fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years, total direct medical costs and total productivity loss costs. Utility and cost model inputs were drawn from published sources. The model adopted a Qatari societal perspective. Sensitivity analyses were performed to test the robustness of estimates. RESULTS Over 10 years among people with T2D, model estimates 108 195 [95% uncertainty interval (UI) 104 249-112 172] non-fatal MIs, 62 366 (95% UI 60 283-65 520) non-fatal strokes and 14 612 (95% UI 14 472-14 744) CVD deaths. The T2D population accrued 4 786 605 (95% UI 4 743 454, 4 858 705) total years of life lived and 3 781 833 (95% UI 3 724 718-3 830 669) total quality-adjusted life years. Direct costs accounted for 57.85% of the total costs, with a projection of QAR41.60 billion (US$11.40 billion) [95% UI 7.53-147.40 billion (US$2.06-40.38 billion)], while the total indirect costs were expected to exceed QAR30.31 billion (US$8.30 billion) [95% UI 1.07-162.60 billion (US$292.05 million-44.55 billion)]. CONCLUSIONS The findings suggest a significant economic and health burden of CVD among people with T2D in Qatar and highlight the need for more enhanced preventive strategies targeting this population group.
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Affiliation(s)
- Dina Abushanab
- Health Economics and Policy Evaluation Research (HEPER) Group Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | | | - Clara Marquina
- Health Economics and Policy Evaluation Research (HEPER) Group Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Danny Liew
- The Adelaide Medical School, The University of Adelaide, Adelaide, Australia
| | - Manal Al-Zaidan
- Department of Pharmacy and Therapeutics Supply, Primary Healthcare Corporation, Doha, Qatar
| | | | - Jazeel Abdulmajeed
- Strategy Planning & Health Intelligence, Primary Healthcare Corporation, Doha, Qatar
| | - Zanfina Ademi
- Health Economics and Policy Evaluation Research (HEPER) Group Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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18
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Kirton A, Jordan LC. Stroke in Children: Key Advances in the Field and the Next 20 Years. Stroke 2024; 55:182-185. [PMID: 38134252 DOI: 10.1161/strokeaha.123.044250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Affiliation(s)
- Adam Kirton
- Departments of Pediatrics and Clinical Neurosciences, Alberta Children's Hospital Research Institute, University of Calgary, Canada (A.K.). Department of Pediatrics, Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, TN (L.C.J.)
| | - Lori C Jordan
- Departments of Pediatrics and Clinical Neurosciences, Alberta Children's Hospital Research Institute, University of Calgary, Canada (A.K.). Department of Pediatrics, Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, TN (L.C.J.)
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Chideme C, Chikobvu D. Application of Time-Series Analysis and Expert Judgment in Modeling and Forecasting Blood Donation Trends in Zimbabwe. MDM Policy Pract 2024; 9:23814683231222483. [PMID: 38250667 PMCID: PMC10798106 DOI: 10.1177/23814683231222483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 10/06/2023] [Indexed: 01/23/2024] Open
Abstract
Background. Blood cannot be artificially manufactured, and there is currently no substitute for human blood. The supply of blood in transfusion facilities requires constant and timely collection of blood from donors. Modeling and forecasting trends in blood collections are critical for determining both the current and future capacity requirements and appropriate models of adequate blood provision. Objectives. The objective of this study is to determine blood collection or donation patterns and develop time-series models that can be updated and refined in predicting future blood donations in Zimbabwe when given the historical data. Materials and Methods. Monthly blood donation data for the period 2009 to 2019 were collected retrospectively from the National Blood Service Zimbabwe database. Time-series models (i.e., the Seasonal Autoregressive Integrated Moving Average [SARIMA] and Error, Trend and Seasonal [ETS]) models were applied and compared. The models were chosen because of their ability to handle the seasonality and other time-series components evident in the blood donation data. Expert opinions and experience were used in selecting the models and in making inferences in the analysis. Results. Time-series plots of blood donations showed seasonal patterns, with significant drops in blood donations in months associated with Zimbabwe's school holidays (April, August, and December) and public holidays. During these holidays, there is a reduced number of school donors, while at about the same time, there is increasing blood demand as a result of road accidents. Model identification procedures established the SARIMA ( 1 , 1 , 2 ) ( 0 , 1 , 1 ) 12 model as the appropriate model for forecasting total blood donation in Zimbabwe. The results and forecasts show an upward trend in blood donations. According to the accuracy measures used, the SARIMA model outperforms the ETS model. Conclusions. Expert knowledge in the blood donation process, coupled with statistical models, can help explain trends exhibited in blood donation data in Zimbabwe. These findings help the blood authorities plan for blood donor campaign drives. The findings are key indicators of where to allocate more resources toward blood donation and when to collect more blood units. The increasing blood donation projections ensure a stable blood bank inventory in the near future. Highlights A SARIMA model can be used to predict the flow of blood donations in Zimbabwe.The seasonal blood donation pattern peaks in the months of March, June/July, and September.The donations troughs are in the months of April, August, December, and January. These are the months coinciding with school holidays in Zimbabwe.Both the SARIMA and ETS models provided similar forecasts, but measures of fit and expert knowledge gave a slight preference to the SARIMA ( 1 , 1 , 2 ) ( 0 , 1 , 1 ) 12 model in predicting the flow of blood donations in Zimbabwe.These model results are useful for guiding allocation of blood donation resources and blood donation drive timing.
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Affiliation(s)
- Coster Chideme
- Department of Mathematical Statistics and Actuarial Sciences, University of the Free State, Bloemfontein, South Africa
| | - Delson Chikobvu
- Department of Mathematical Statistics and Actuarial Sciences, University of the Free State, Bloemfontein, South Africa
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20
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Hoyos W, Hoyos K, Ruiz-Pérez R. Artificial intelligence model for early detection of diabetes. Biomedica 2023; 43:110-121. [PMID: 38207148 PMCID: PMC10946312 DOI: 10.7705/biomedica.7147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/10/2023] [Indexed: 01/13/2024]
Abstract
Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decisionmaking in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.
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Affiliation(s)
- William Hoyos
- Grupo de Investigación en Ingeniería Sostenible e Inteligente, Universidad Cooperativa de Colombia, Montería, Colombia; Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia.
| | - Kenia Hoyos
- Laboratorio Clínico, Clínica Salud Social, Sincelejo, Colombia.
| | - Rander Ruiz-Pérez
- Grupo de Investigación Interdisciplinario del Bajo Cauca y Sur de Córdoba, Universidad de Antioquia, Medellín, Colombia.
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21
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Fitriana PM, Saputra J, Halim ZA. The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach. Big Data 2023. [PMID: 38117613 DOI: 10.1089/big.2023.0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.
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Affiliation(s)
- Pingkan Mayosi Fitriana
- Department of Economics, Faculty of Business, Economics, and Social Development, Universiti Malaysia Terengganu, Terengganu, Malaysia
| | - Jumadil Saputra
- Department of Economics, Faculty of Business, Economics, and Social Development, Universiti Malaysia Terengganu, Terengganu, Malaysia
| | - Zairihan Abdul Halim
- Department of Economics, Faculty of Business, Economics, and Social Development, Universiti Malaysia Terengganu, Terengganu, Malaysia
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22
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Marx A, Di Stefano F, Leutheuser H, Chin-Cheong K, Pfister M, Burckhardt MA, Bachmann S, Vogt JE. Blood glucose forecasting from temporal and static information in children with T1D. Front Pediatr 2023; 11:1296904. [PMID: 38155742 PMCID: PMC10752933 DOI: 10.3389/fped.2023.1296904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Background The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30 min) and long-term (2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.
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Affiliation(s)
- Alexander Marx
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Marc Pfister
- Pediatric Pharmacology and Pharmacometrics, University Children’s Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Endocrinolgy and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
| | - Sara Bachmann
- Department of Clinical Research, University Hospital Basel, Basel, Switzerland
- Pediatric Endocrinolgy and Diabetology, University Children’s Hospital Basel, Basel, Switzerland
| | - Julia E. Vogt
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
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23
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Noh E, Hong J, Yoo J, Jung J. Inference and forecasting phase shift regime of COVID-19 sub-lineages with a Markov-switching model. Microbiol Spectr 2023; 11:e0166923. [PMID: 37811981 PMCID: PMC10714866 DOI: 10.1128/spectrum.01669-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/23/2023] [Indexed: 10/10/2023] Open
Abstract
IMPORTANCE Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.
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Affiliation(s)
- Eul Noh
- Freddie Mac, Tysons Corner, Virginia, USA
| | - Jinwook Hong
- Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Joonkyung Yoo
- Department of Economics, Rutgers University--New Brunswick, New Brunswick, New Jersey, USA
| | - Jaehun Jung
- Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
- Department of Preventive Medicine, Gachon University College of Medicine, Incheon, South Korea
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El-Warrak L, Nunes M, Luna G, Barbosa CE, Lyra A, Argôlo M, Lima Y, Salazar H, de Souza JM. Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era. Healthcare (Basel) 2023; 11:3118. [PMID: 38132008 PMCID: PMC10743190 DOI: 10.3390/healthcare11243118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/13/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
The COVID-19 pandemic, a transformative event in modern society, has disrupted routine, work, behavior, and human relationships. Organizations, amidst the chaos, have innovatively adapted to the evolving situation. However, many countries were unprepared for the magnitude of the challenge, revealing the fragility of health responses due to inadequate leadership, insufficient resources, and poor information system integration. Structural changes in health systems are imperative, particularly in leadership, governance, human resources, financing, information systems, technology, and health service provision. This research utilizes the Technological Roadmapping method to analyze the health sector, focusing on public health, drawing on articles from SCOPUS and PubMed databases, and creating a roadmap extending to 2050. The research presents three long-term scenarios based on the literature-derived roadmap and explores various alternatives, including integrated care, telemedicine, Big Data utilization, nanotechnology, and Big Tech's AI services. The results underscore the anticipation of post-pandemic public health with high expectations, emphasizing the importance of integrating health history access, encouraging self-care, and leveraging technology for streamlined treatment. Practical implications include insights for decision makers and stakeholders to inform strategic planning and adapt to evolving industry demands, recognizing the significance of preventive services and the humanizing potential of technology.
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Affiliation(s)
- Leonardo El-Warrak
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Mariano Nunes
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Gabriel Luna
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Carlos Eduardo Barbosa
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
- Centro de Análises de Sistemas Navais, Rio de Janeiro 20091-000, Brazil
| | - Alan Lyra
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Matheus Argôlo
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Yuri Lima
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Herbert Salazar
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
| | - Jano Moreira de Souza
- Graduate School of Engineering (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Avenida Horácio Macedo 2030, Centro de Tecnologia, Bloco H, Rio de Janeiro 21941-972, Brazil; (L.E.-W.); (G.L.); (Y.L.); (H.S.)
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Silva ABDS, Costa LS, de Frias PG, Araújo ACDM, do Bonfim CV. Temporal analysis of mortality from preventable causes in the first 24 hours of life, 2000-2021. Rev Lat Am Enfermagem 2023; 31:e4079. [PMID: 38055593 PMCID: PMC10695294 DOI: 10.1590/1518-8345.6696.4079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/06/2023] [Indexed: 12/08/2023] Open
Abstract
OBJECTIVE to analyze the temporal pattern and estimate mortality rates in the first 24 hours of life and from preventable causes in the state of Pernambuco from 2000 to 2021. METHOD an ecological study, using the quarter as the unit of analysis. The data source was made up of the Mortality Information System and the Live Birth Information System. The time series modeling was conducted according to the Autoregressive Integrated Moving Average Model. RESULTS 14,462 deaths were recorded in the first 24 hours of life, 11,110 (76.8%) of which being preventable. It is observed from the forecasts that the mortality rate in the first 24 hours of life ranged from 3.3 to 2.4 per 1,000 live births, and the mortality rate from preventable causes ranged from 2.3 to 1.8 per 1,000 live births. CONCLUSION the prediction suggested progress in reducing mortality in the first 24 hours of life in the state and from preventable causes. The ARIMA models presented satisfactory estimates for mortality rates and preventable causes in the first 24 hours of life.
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Affiliation(s)
- Aline Beatriz dos Santos Silva
- Universidade Federal de Pernambuco, Recife, PE, Brasil
- Instituto Aggeu Magalhães-Fiocruz, Recife, PE, Brasil
- Becaria de la Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brasil
| | | | | | - Ana Catarina de Melo Araújo
- Secretaria Estadual de Saúde de Pernambuco, Superintendência de Imunizações e Doenças Imunopreveníveis, Recife, PE, Brasil
| | - Cristine Vieira do Bonfim
- Universidade Federal de Pernambuco, Recife, PE, Brasil
- Fundação Joaquim Nabuco, Diretoria de Pesquisas Sociais, Recife, PE, Brasil
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Felici-Castell S, Segura-Garcia J, Perez-Solano JJ, Fayos-Jordan R, Soriano-Asensi A, Alcaraz-Calero JM. AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems. Sensors (Basel) 2023; 23:9585. [PMID: 38067957 PMCID: PMC10708678 DOI: 10.3390/s23239585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023]
Abstract
The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%.
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Affiliation(s)
- Santiago Felici-Castell
- Escola Tècnica Superior d’Enginyeria, Universitat de València, Campus de Burjassot, 46100 Valencia, Spain; (J.J.P.-S.); (A.S.-A.)
| | - Jaume Segura-Garcia
- Escola Tècnica Superior d’Enginyeria, Universitat de València, Campus de Burjassot, 46100 Valencia, Spain; (J.J.P.-S.); (A.S.-A.)
| | - Juan J. Perez-Solano
- Escola Tècnica Superior d’Enginyeria, Universitat de València, Campus de Burjassot, 46100 Valencia, Spain; (J.J.P.-S.); (A.S.-A.)
| | - Rafael Fayos-Jordan
- School of Computing, Engineering and Physical Sciences, University of West of Scotland, Storie Street, Paisley PA1 2HB, UK; (R.F.-J.); (J.M.A.-C.)
| | - Antonio Soriano-Asensi
- Escola Tècnica Superior d’Enginyeria, Universitat de València, Campus de Burjassot, 46100 Valencia, Spain; (J.J.P.-S.); (A.S.-A.)
| | - Jose M. Alcaraz-Calero
- School of Computing, Engineering and Physical Sciences, University of West of Scotland, Storie Street, Paisley PA1 2HB, UK; (R.F.-J.); (J.M.A.-C.)
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N Naumova E. Forecasting Seasonal Acute Malnutrition: Setting the Framework. Food Nutr Bull 2023; 44:S83-S93. [PMID: 37850923 DOI: 10.1177/03795721231202238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
BACKGROUND Malnutrition is an umbrella term that refers to an impairment in nutrition indicative of subsequently compromised human well-being. The term covers the full spectrum of nutritional impairments from a small yet detectable departure from a "norm" to a terminal stage when severe malnutrition could result in death. This broad spectrum of nutritional departures from "the optimum" dictates the need for an ensemble of metrics to capture the complexity of involved mechanisms, risk factors, precipitating events, short-term, and long-term consequences. Ideally, these metrics should be universally applicable to vulnerable populations, settings, ages, and times when people are most susceptible to malnutrition. We should be able to characterize and intervene to minimize the risk of malnutrition, especially child acute malnutrition that could be assessed by anthropometric measurements. OBJECTIVES The main challenge in reaching such an ambitious goal is the complexity of measuring, characterizing, explaining, predicting, and preventing malnutrition at any dimension: temporal or spatial and at any scale: a person or a group. The expansive body of literature has been accumulated on many temporal aspects of malnutrition and seasonal changes in nutritional (anthropometric) status. The research community is now shifting their attention to predictive modeling of child malnutrition and its importance for clinical and public health interventions. This communication aims to provide an overview of challenges for understanding child malnutrition from a perspective of predictive modeling focusing on well-documented seasonal variations in nutritional outcomes and exploring "the systems approach" to tackle underlining conceptual and practical complexities to forecast seasonal malnutrition in an accurate and timely manner. This generalized approach to forecasting seasonal malnutrition is then applied specifically to child acute malnutrition.
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Affiliation(s)
- Elena N Naumova
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
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Waked JP, de Aguiar CS, Aroucha JMCNL, Godoy GP, de Melo REVA, Caldas A. Predictive model for temporomandibular disorder in adolescents: Decision tree. Int J Paediatr Dent 2023. [PMID: 38013209 DOI: 10.1111/ipd.13137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/04/2023] [Accepted: 10/17/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Temporomandibular disorders (TMD) do not only occur in adults but also in adolescents, with negative impacts on their development. AIM To propose a predictive model for TMD in adolescents using a decision tree (DT) analysis and to identify groups at high and low risk of developing TMD in the city of Recife, PE, Brazil. DESIGN This cross-sectional study was conducted in Recife on 1342 schoolchildren of both sexes aged 10-17 years. The analyses were performed using Pearson's chi-squared test and Fisher's exact test, as well as the CHAID algorithm for the construction of the DT. The SPSS statistical program was used. RESULTS The prevalence of TMD was 33.2%. Statistically significant associations were observed between TMD and sex, depression, self-reported orofacial pain, and orofacial pain on clinical examination. The DT consisted of self-reported orofacial pain, orofacial pain on physical examination, and depression, with an overall predictive power of 73.0%. CONCLUSION The proposed tree has a good predictive capacity and permits to identify groups at high risk of developing TMD among adolescents, such as those with self-reported orofacial pain or orofacial pain on examination associated with depression.
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Affiliation(s)
- Jorge Pontual Waked
- Academic Unit of Biological Sciences, Center for Rural Health and Technology, Federal University of Campina Grande, Patos, Brazil
| | - Camilla Siqueira de Aguiar
- Department of Prosthesis and Oral Surgery, Health Science Center, Federal University of Pernambuco, Recife, Brazil
| | | | - Gustavo Pina Godoy
- Post-Graduation Programme of Dentistry, Federal University of Pernambuco, Recife, Brazil
| | | | - Arnaldo Caldas
- Post-Graduation Programme of Dentistry, Federal University of Pernambuco, Recife, Brazil
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Koichubekov B, Takuadina A, Korshukov I, Sorokina M, Turmukhambetova A. The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling. Healthcare (Basel) 2023; 11:2968. [PMID: 37998460 PMCID: PMC10671669 DOI: 10.3390/healthcare11222968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.
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Affiliation(s)
- Berik Koichubekov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Aliya Takuadina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Ilya Korshukov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Marina Sorokina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Anar Turmukhambetova
- Institute of Life Sciences, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan;
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Knab F, Koch SP, Major S, Farr TD, Mueller S, Euskirchen P, Eggers M, Kuffner MT, Walter J, Berchtold D, Knauss S, Dreier JP, Meisel A, Endres M, Dirnagl U, Wenger N, Hoffmann CJ, Boehm-Sturm P, Harms C. Prediction of Stroke Outcome in Mice Based on Noninvasive MRI and Behavioral Testing. Stroke 2023; 54:2895-2905. [PMID: 37746704 PMCID: PMC10589430 DOI: 10.1161/strokeaha.123.043897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/06/2023] [Accepted: 08/02/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Prediction of poststroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. METHODS We intended to incorporate heterogeneity into our retrospective study to broaden the applicability of our findings and prediction tools. We therefore analyzed the effect of 30, 45, and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were coregistered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. RESULTS Variance of stroke volumes was increased by 45 minutes of arterial occlusion compared with 60 minutes. The inclusion of various genotypes enhanced heterogeneity further. We detected both a subacute and residual motor-functional deficit after stroke in mice and different recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomic regions within the ischemic lesion had particular impact on the prediction of long-term outcomes. Prediction accuracy depended on the degree of functional impairment. CONCLUSIONS For the first time, we developed and validated a robust tool for the prediction of functional outcomes after experimental stroke in mice using a large and genetically heterogeneous cohort. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.
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Affiliation(s)
- Felix Knab
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Stefan Paul Koch
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Sebastian Major
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Tracy D. Farr
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
- School of Life Sciences, University of Nottingham, United Kingdom (T.D.F.)
| | - Susanne Mueller
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Philipp Euskirchen
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Moritz Eggers
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Melanie T.C. Kuffner
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Josefine Walter
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, QUEST Center for Transforming Biomedical Research, Germany (J.W.)
| | - Daniel Berchtold
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Samuel Knauss
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
| | - Jens P. Dreier
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- Bernstein Center for Computational Neuroscience (J.P.D.)
| | - Andreas Meisel
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Matthias Endres
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany (M. Endres., U.D.)
- German Center for Neurodegenerative Diseases (DZNE) (M. Endres, U.D.)
| | - Ulrich Dirnagl
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany (M. Endres., U.D.)
- German Center for Neurodegenerative Diseases (DZNE) (M. Endres, U.D.)
| | - Nikolaus Wenger
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
| | - Christian J. Hoffmann
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
| | - Philipp Boehm-Sturm
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Christoph Harms
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
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Luquet M, Poggi S, Buchard C, Plantegenest M, Tricault Y. Predicting the seasonal flight activity of Myzus persicae, the main aphid vector of Virus Yellows in sugar beet. Pest Manag Sci 2023; 79:4508-4520. [PMID: 37421357 DOI: 10.1002/ps.7653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/04/2023] [Accepted: 07/08/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND Virus Yellows (VY), a disease caused by several aphid-borne viruses, is a major threat to the global sugar beet production. Following the ban of neonicotinoid-based seed treatments against aphids in Europe, increased efforts are needed to monitor and forecast aphid population spread during the sugar beet growing season. In particular, predicting aphid flight seasonal activity could allow anticipation of the timing and intensity of crop colonisation and contribute to the proper implementation of management methods. Forecasts should be made early enough to assess risk, but can be updated as the season progresses to refine management. Based on a long-term suction-trap dataset gathered between 1978 and 2014, we built and evaluated a set of models to predict the flight activity features of the main VY vector, Myzus persicae, at any location in the French sugar beet production area (c. 4 × 105 ha). Flight onset dates, length of flight period and cumulative abundance of flying aphids were predicted using climatic and land-use predictors as well as geographical position. RESULTS Our predictions outperformed current models published in the literature. The importance of the predictor variables varied according to the predicted flight feature but winter and early spring temperature always played a major role. Forecasts based on temperature were made more accurate by adding predictors related to aphid winter reservoirs. In addition, updating the model parameters to take advantage of new weather data acquired during the season improved the flight forecast. CONCLUSION Our models can be used as a tool for the mitigation in sugar beet crops. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Martin Luquet
- IGEPP, INRAE, Institut Agro, Université de Rennes, Angers, France
| | - Sylvain Poggi
- IGEPP, INRAE, Institut Agro, Université de Rennes, Le Rheu, France
| | | | | | - Yann Tricault
- IGEPP, INRAE, Institut Agro, Université de Rennes, Angers, France
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Gantenberg JR, McConeghy KW, Howe CJ, Steingrimsson J, van Aalst R, Chit A, Zullo AR. Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study. Am J Epidemiol 2023; 192:1688-1700. [PMID: 37147861 PMCID: PMC10558190 DOI: 10.1093/aje/kwad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 08/17/2022] [Accepted: 04/27/2023] [Indexed: 05/07/2023] Open
Abstract
Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.
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Affiliation(s)
- Jason R Gantenberg
- Correspondence to Dr. Jason R. Gantenberg, Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI 02912 (e-mail: )
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Su SY. Synthesized Age-Period-Cohort Prediction Method: Application to Lung Cancer Mortality in Taiwan. Am J Epidemiol 2023; 192:1712-1719. [PMID: 37218606 DOI: 10.1093/aje/kwad120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 04/18/2023] [Accepted: 05/16/2023] [Indexed: 05/24/2023] Open
Abstract
Age-period-cohort analysis involves 3 temporal factors: age (the length of time from birth to diagnosis), period (the calendar time of diagnosis), and cohort (the calendar time of birth). The application of age-period-cohort analysis in disease forecasting can help researchers and health authorities anticipate future disease burden. In this study, a synthesized age-period-cohort prediction method was proposed based on 4 assumptions: 1) no single model can dominate as the most accurate prediction model in all forecasting scenarios; 2) historical trends will not continue indefinitely; 3) a model with the most accurate forecast for the training data will also be appropriate for forecasting future data; and 4) a model dominated by the stochastic temporal change will be the best-selected model with the robust forecasting. An ensemble of age-period-cohort prediction models was constructed, and Monte Carlo cross-validation was performed to evaluate forecasting accuracy of these models. Data on lung cancer mortality from 1996 to 2015 in Taiwan were used and projected to the year 2035 to illustrate the method. The actual lung cancer mortality rates from 2016 to 2020 were then used to verify the forecasting accuracy.
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Brück CC, Wolters FJ, Ikram MA, de Kok IMCM. Projections of costs and quality adjusted life years lost due to dementia from 2020 to 2050: A population-based microsimulation study. Alzheimers Dement 2023; 19:4532-4541. [PMID: 36916447 DOI: 10.1002/alz.13019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 03/15/2023]
Abstract
INTRODUCTION Efficient healthcare planning requires reliable projections of the future increase in costs and quality-adjusted life years (QALYs) lost due to dementia. METHODS We used the microsimulation model MISCAN-Dementia to simulate life histories and dementia occurrence using population-based Rotterdam Study data and nationwide birth cohort demographics. We estimated costs and QALYs lost in the Netherlands from 2020 to 2050, incorporating literature estimates of cost and utility for patients and caregivers by dementia severity and care setting. RESULTS Societal costs and QALYs lost due to dementia are estimated to double between 2020 and 2050. Costs are incurred predominantly through institutional (34%), formal home (31%), and informal home care (20%). Lost QALYs are mostly due to shortened life expectancy (67%) and, to a lesser extent, quality of life with severe dementia (14%). DISCUSSION To limit healthcare costs and quality of life losses due to dementia, interventions are needed that slow symptom progression and reduce care dependency.
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Affiliation(s)
- Chiara C Brück
- Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
| | - Frank J Wolters
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine and Alzheimer Center, Erasmus MC, Rotterdam, The Netherlands
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Du M, Mo Y, Li A, Ge S, Peres MA. Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis. J Periodontol 2023; 94:1254-1265. [PMID: 37133974 DOI: 10.1002/jper.23-0088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/13/2023] [Accepted: 04/15/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND The performance of the 2018 European Federation of Periodontology/American Academy of Periodontology (EFP/AAP) classification of periodontitis for epidemiology surveillance purposes remains to be investigated. This study assessed the surveillance use of the 2018 EFP/AAP classification and its agreement with the unsupervised clustering method compared with the 2012 Centers for Disease Control and Prevention(CDC)/AAP case definition. METHODS Participants (n = 9424) in the National Health and Nutrition Examination Survey (NHANES) were staged by the 2018 EFP/AAP classification and classified into subgroups via k-medoids clustering. Concordance levels between periodontitis definitions and the clustering method were evaluated through the multiclass area under the receiver operating characteristic curve (multiclass AUC) among "periodontitis cases" and the general population, respectively. The multiclass AUC of the 2012 CDC/AAP definition versus clustering was used as a reference. The associations of periodontitis with chronic diseases were estimated using multivariable logistic regression. RESULTS All the participants were identified as "periodontitis cases" by the 2018 EFP/AAP classification, and the prevalence of stage III-IV was 30%. The optimal numbers of clusters were three and four. The 2012 CDC/AAP definition versus clustering yielded a multiclass AUC of 0.82 and 0.85 among the general population and "periodontitis cases," respectively. The multiclass AUC of the 2018 EFP/AAP classification versus clustering was 0.77 and 0.78 for different target populations. Similar patterns prevailed in associations with chronic diseases between the 2018 EFP/AAP classification and clustering. CONCLUSIONS The validity of the 2018 EFP/AAP classification was verified by the unsupervised clustering method, which performed better in distinguishing "periodontitis cases" than classifying the general population. For surveillance purposes, the 2012 CDC/AAP definition showed a higher agreement level with the clustering method than the 2018 EFP/AAP classification.
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Affiliation(s)
- Mi Du
- Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Yuanqiu Mo
- Department of System Science, School of Mathematics, Southeast University, Nanjing, China
| | - An Li
- Department of Periodontology, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China
| | - Shaohua Ge
- Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Marco A Peres
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore
- Oral Health ACP, Health Service and Systems Research Programme, Duke-NUS Medical School, Singapore
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Servadio JL, Convertino M, Fiecas M, Muñoz‐Zanzi C. Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics. Geohealth 2023; 7:e2023GH000870. [PMID: 37885914 PMCID: PMC10599710 DOI: 10.1029/2023gh000870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/31/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.
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Affiliation(s)
- Joseph L. Servadio
- Department of BiologyCenter for Infectious Disease DynamicsPennsylvania State UniversityUniversity ParkPAUSA
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Mark Fiecas
- Division of BiostatisticsSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | - Claudia Muñoz‐Zanzi
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
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Shorey S, Lalor J, Pereira TLB, Jarašiūnaitė-Fedosejeva G, Downe S. Decision-making and future pregnancies after a positive fetal anomaly screen: A scoping review. J Clin Nurs 2023; 32:5534-5549. [PMID: 36707923 DOI: 10.1111/jocn.16628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/07/2022] [Accepted: 01/04/2023] [Indexed: 01/29/2023]
Abstract
AIMS AND OBJECTIVES To examine and consolidate literature on the experiences and decision-making of parents following a screen positive result for a potential fetal anomaly and/or diagnosis of an actual anomaly in a previous pregnancy. BACKGROUND Prenatal screening consists of any diagnostic modality that is aimed at acquiring information about a fetus or an embryo; however, the entire process is highly stressful for parents, especially if there was a previous screen positive result, but no abnormality was detected in the final result. METHODS Eight electronic databases (PubMed, Embase, CINAHL, PsycINFO, Scopus, Web of Science, ProQuest Theses and Dissertations and ClinicalTrials.gov) were searched from each database's inception until February 2022. This scoping review was guided by Arksey and O'Malley's framework and was reported in accordance with the PRISMA-ScR checklist. Braun and Clarke's thematic analysis framework was utilised. RESULTS Thirty-one studies were eligible for inclusion. Two main themes (reliving the fear while maintaining hope, and bridging the past and future pregnancies) and six subthemes were identified. CONCLUSIONS A fetal anomaly diagnosis in pregnancy had a mixed impact on the attitudes of parents toward a future pregnancy. Some parents were fearful of reliving a traumatic experience, while others were determined to have a healthy child and grow their family. Parents generally expressed a greater preference for non-invasive over invasive prenatal testing due to the procedural risks involved. RELEVANCE TO CLINICAL PRACTICE There is a need for healthcare professionals to provide psychosocial and emotional support to parents so that they can achieve resolution for their previous pregnancy. Healthcare professionals' ability to provide informational support also enables these parents to make informed decision and understand their reproductive outcomes. Additionally, healthcare administration and policymakers should reconsider current neonatal or pregnancy loss bereavement guidelines to improve the inclusivity of fathers. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.
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Affiliation(s)
- Shefaly Shorey
- Alice Lee Center for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joan Lalor
- School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland
| | - Travis Lanz-Brian Pereira
- Alice Lee Center for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Soo Downe
- THRIVE Centre, School of Community Health and Midwifery, University of Central Lancashire, Preston, UK
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Rusin CG, Acosta SI, Brady KM, Vu E, Scahill C, Fonseca B, Barrett C, Simsic J, Yates AR, Klepczynski B, Gaynor WJ, Penny DJ. Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study. JTCVS Open 2023; 15:406-411. [PMID: 37808061 PMCID: PMC10556807 DOI: 10.1016/j.xjon.2023.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/13/2023] [Accepted: 05/02/2023] [Indexed: 10/10/2023]
Abstract
Objectives Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes. Methods A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm. Results Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927). Conclusions Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.
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Affiliation(s)
- Craig G. Rusin
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
| | - Sebastian I. Acosta
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
| | - Kennith M. Brady
- Department of Anesthesiology, Northwestern University, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill
| | - Eric Vu
- Department of Anesthesiology, Northwestern University, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill
| | - Carly Scahill
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Brian Fonseca
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Cindy Barrett
- Department of Pediatrics—Cardiology, Children's Hospital Colorado, Aurora, Colo
| | - Janet Simsic
- Department of Pediatrics—Cardiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Andrew R. Yates
- Department of Pediatrics—Cardiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Brenna Klepczynski
- Department of Cardiovascular Surgery, Children's Hospital of Philadelphia, Philadelphia, Pa
| | - William J. Gaynor
- Department of Cardiovascular Surgery, Children's Hospital of Philadelphia, Philadelphia, Pa
| | - Daniel J. Penny
- Department of Pediatrics—Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex
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Stoner O, Halliday A, Economou T. Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method. Biometrics 2023; 79:2537-2550. [PMID: 36484382 PMCID: PMC9877609 DOI: 10.1111/biom.13810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/18/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022]
Abstract
The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID-19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision-making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15-month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID-19 and future epidemics.
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Affiliation(s)
- Oliver Stoner
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Alba Halliday
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Theo Economou
- Climate and Atmosphere Research CentreThe Cyprus InstituteAglantziaCyprus
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Herbert P, Hou K, Bradley C, Hager G, Boland MV, Ramulu P, Unberath M, Yohannan J. Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data. Ophthalmol Glaucoma 2023; 6:466-473. [PMID: 36944385 PMCID: PMC10509314 DOI: 10.1016/j.ogla.2023.03.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/20/2023] [Accepted: 03/10/2023] [Indexed: 03/22/2023]
Abstract
PURPOSE To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN A retrospective cohort study. SUBJECTS In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). METHODS We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES The AUC of DLMs when forecasting rapidly worsening eyes. RESULTS Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). CONCLUSIONS Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Patrick Herbert
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Kaihua Hou
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Greg Hager
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Michael V Boland
- Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland
| | - Mathias Unberath
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
| | - Jithin Yohannan
- Malone Center For Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland; Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland.
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Gerdtsson A, Torisson G, Thor A, Grenabo Bergdahl A, Almås B, Håkansson U, Törnblom M, Negaard HFS, Glimelius I, Halvorsen D, Karlsdóttir Á, Haugnes HS, Larsen SM, Holmberg G, Wahlqvist R, Tandstad T, Cohn-Cedermark G, Ståhl O, Kjellman A. Validation of a prediction model for post-chemotherapy fibrosis in nonseminoma patients. BJU Int 2023; 132:329-336. [PMID: 37129962 DOI: 10.1111/bju.16040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
OBJECTIVE To validate Vergouwe's prediction model using the Swedish and Norwegian Testicular Cancer Group (SWENOTECA) RETROP database and to define its clinical utility. MATERIALS AND METHODS Vergouwe's prediction model for benign histopathology in post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) uses the following variables: presence of teratoma in orchiectomy specimen; pre-chemotherapy level of alpha-fetoprotein; β-Human chorionic gonadotropin and lactate dehydrogenase; and lymph node size pre- and post-chemotherapy. Our validation cohort consisted of patients included in RETROP, a prospective population-based database of patients in Sweden and Norway with metastatic nonseminoma, who underwent PC-RPLND in the period 2007-2014. Discrimination and calibration analyses were used to validate Vergouwe's prediction model results. Calibration plots were created and a Hosmer-Lemeshow test was calculated. Clinical utility, expressed as opt-out net benefit (NBopt-out ), was analysed using decision curve analysis. RESULTS Overall, 284 patients were included in the analysis, of whom 130 (46%) had benign histology after PC-RPLND. Discrimination analysis showed good reproducibility, with an area under the receiver-operating characteristic curve (AUC) of 0.82 (95% confidence interval 0.77-0.87) compared to Vergouwe's prediction model (AUC between 0.77 and 0.84). Calibration was acceptable with no recalibration. Using a prediction threshold of 70% for benign histopathology, NBopt-out was 0.098. Using the model and this threshold, 61 patients would have been spared surgery. However, only 51 of 61 were correctly classified as benign. CONCLUSIONS The model was externally validated with good reproducibility. In a clinical setting, the model may identify patients with a high chance of benign histopathology, thereby sparing patients of surgery. However, meticulous follow-up is required.
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Affiliation(s)
- Axel Gerdtsson
- Division of Urology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Department of Urology, Skåne University Hospital, Malmö, Sweden
| | - Gustav Torisson
- Department of Translational Medicine, Lund University, Lund, Sweden
| | - Anna Thor
- Division of Urology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Department of Urology, Pelvic Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Grenabo Bergdahl
- Department of Urology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Urology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenborg, Sweden
| | - Bjarte Almås
- Department of Urology, Haukeland University Hospital, Bergen, Norway
| | | | - Magnus Törnblom
- Section of Urology, Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
- Department of Surgery, Visby County Hospital, Visby, Sweden
| | | | - Ingrid Glimelius
- Department of Immunology, Genetics and Pathology, Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
| | - Dag Halvorsen
- Department of Urology, St. Olavs University Hospital, Trondheim, Norway
| | - Ása Karlsdóttir
- Department of Oncology, Haukeland University Hospital, Bergen, Norway
| | - Hege Sagstuen Haugnes
- Department of Oncology, University Hospital of North Norway, Tromsø, Norway
- Department of Clinical Medicine, UIT-The Arctic University of Norway, Tromsø, Norway
| | | | - Göran Holmberg
- Department of Urology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenborg, Sweden
| | - Rolf Wahlqvist
- Department of Urology, Oslo University Hospital, Oslo, Norway
| | - Torgrim Tandstad
- The Cancer Clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, The Norwegian University of Science and Technology, Trondheim, Norway
| | - Gabriella Cohn-Cedermark
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Pelvic Cancer, Genitourinary Oncology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Olof Ståhl
- Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Oncology, Skåne University Hospital, Lund, Sweden
| | - Anders Kjellman
- Division of Urology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Department of Urology, Pelvic Cancer, Karolinska University Hospital, Stockholm, Sweden
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Onambele L, Guillen-Aguinaga S, Guillen-Aguinaga L, Ortega-Leon W, Montejo R, Alas-Brun R, Aguinaga-Ontoso E, Aguinaga-Ontoso I, Guillen-Grima F. Trends, Projections, and Regional Disparities of Maternal Mortality in Africa (1990-2030): An ARIMA Forecasting Approach. Epidemiologia (Basel) 2023; 4:322-351. [PMID: 37754279 PMCID: PMC10528291 DOI: 10.3390/epidemiologia4030032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/03/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
With the United Nations Sustainable Development Goals (SDG) (2015-2030) focused on the reduction in maternal mortality, monitoring and forecasting maternal mortality rates (MMRs) in regions like Africa is crucial for health strategy planning by policymakers, international organizations, and NGOs. We collected maternal mortality rates per 100,000 births from the World Bank database between 1990 and 2015. Joinpoint regression was applied to assess trends, and the autoregressive integrated moving average (ARIMA) model was used on 1990-2015 data to forecast the MMRs for the next 15 years. We also used the Holt method and the machine-learning Prophet Forecasting Model. The study found a decline in MMRs in Africa with an average annual percentage change (APC) of -2.6% (95% CI -2.7; -2.5). North Africa reported the lowest MMR, while East Africa experienced the sharpest decline. The region-specific ARIMA models predict that the maternal mortality rate (MMR) in 2030 will vary across regions, ranging from 161 deaths per 100,000 births in North Africa to 302 deaths per 100,000 births in Central Africa, averaging 182 per 100,000 births for the continent. Despite the observed decreasing trend in maternal mortality rate (MMR), the MMR in Africa remains relatively high. The results indicate that MMR in Africa will continue to decrease by 2030. However, no region of Africa will likely reach the SDG target.
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Affiliation(s)
- Luc Onambele
- School of Health Sciences, Catholic University of Central Africa, Yaoundé 1110, Cameroon;
| | - Sara Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Laura Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Department of Nursing, Suldal Sykehjem, 4230 Sands, Norway
| | - Wilfrido Ortega-Leon
- Department of Surgery, Medical and Social Sciences, University of Alcala de Henares, 28871 Alcalá de Henares, Spain;
| | - Rocio Montejo
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, 413 46 Gothenburg, Sweden;
- Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, 413 46 Gothenburg, Sweden
| | - Rosa Alas-Brun
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | | | - Ines Aguinaga-Ontoso
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Area of Epidemiology and Public Health, Healthcare Research Institute of Navarre (IdiSNA), 31008 Pamplona, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, 46980 Madrid, Spain
| | - Francisco Guillen-Grima
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Area of Epidemiology and Public Health, Healthcare Research Institute of Navarre (IdiSNA), 31008 Pamplona, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, 46980 Madrid, Spain
- Department of Preventive Medicine, Clínica Universidad de Navarra, 31008 Pamplona, Spain
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Hsu CY, Yang CH, Tung MC, Liu HJ, Ou YC. Theranostic Robot-Assisted Radical Prostatectomy: Things Understood and Not Understood. Cancers (Basel) 2023; 15:4288. [PMID: 37686563 PMCID: PMC10486521 DOI: 10.3390/cancers15174288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
OBJECTIVE This study aimed to explore the benefits of theranostic robot-assisted radical prostatectomy (T-RARP) for clinically highly suspicious prostate cancer (PCa) without proven biopsies. MATERIAL AND METHODS Between February 2016 and December 2020, we included men with clinically highly suspicious PCa in this study. They were assessed to have possible localized PCa without any initial treatments, and were categorized into previous benign biopsies or without biopsies. Furthermore, another group of malignant biopsies with RARP in the same time frame was adopted as the control group. The endpoints were to compare the oncological outcome and functional outcome between malignant biopsies with RARP and T-RARP. p < 0.05 was considered to be significant. RESULTS We included 164 men with proven malignant biopsies treated with RARP as the control group. For T-RARP, we included 192 men. Among them, 129 were preoperatively benign biopsies, and 63 had no biopsies before T-RARP. Approximately 75% of men in the T-RARP group had malignant pathology in their final reports, and the other 25% had benign pathology. T-RARP provides several oncological advantages, such as a higher initial pathological T stage, lower Gleason grade, and lower odds of positive surgical margins. However, the biochemical recurrence rates were not significantly decreased. From our cohort, T-RARP (odds ratio with 95% confidence interval; erectile recovery: 3.19 (1.84-5.52), p < 0.001; continence recovery: 2.25 (1.46-3.48), p < 0.001) could result in better recovery of functional outcomes than malignant biopsies with RARP. CONCLUSIONS For clinically highly suspicious PCa, T-RARP was able to detect around 75% of PCa cases and preserved their functional outcomes maximally. However, in 25% of men with benign pathology, approximately 6% would have incontinence and 10% would have erectile impairment. This part should be sufficiently informed of the potential groups considering T-RARP.
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Affiliation(s)
- Chao-Yu Hsu
- Division of Urology, Department of Surgery, Tungs’ Taichung MetroHarbor Hospital, Taichung 435, Taiwan; (C.-Y.H.); (C.-H.Y.); (M.-C.T.)
- Doctoral Program in Translational Medicine, National Chung Hsing University, Taichung 402, Taiwan
| | - Che-Hsueh Yang
- Division of Urology, Department of Surgery, Tungs’ Taichung MetroHarbor Hospital, Taichung 435, Taiwan; (C.-Y.H.); (C.-H.Y.); (M.-C.T.)
| | - Min-Che Tung
- Division of Urology, Department of Surgery, Tungs’ Taichung MetroHarbor Hospital, Taichung 435, Taiwan; (C.-Y.H.); (C.-H.Y.); (M.-C.T.)
| | - Hung-Jen Liu
- Doctoral Program in Translational Medicine, National Chung Hsing University, Taichung 402, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, Taichung 402, Taiwan
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung 402, Taiwan
- Rong Hsing Translational Medicine Research Center, National Chung Hsing University, Taichung 402, Taiwan
- Department of Life Sciences, National Chung Hsing University, Taichung 402, Taiwan
| | - Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tungs’ Taichung MetroHarbor Hospital, Taichung 435, Taiwan; (C.-Y.H.); (C.-H.Y.); (M.-C.T.)
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Castro AF, Li W, Bernard-Davila B, Huynh M, Van Wye G. Recent Advances in the Use of the Mortality Syndromic Surveillance System-New York City, 2015-2020. Public Health Rep 2023:333549231190115. [PMID: 37610119 DOI: 10.1177/00333549231190115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023] Open
Abstract
OBJECTIVE New York City's automated mortality syndromic surveillance system monitors temporal and spatial patterns in mortality. To describe the use of the syndromic surveillance system, we used the system to find mortality patterns for the 15 leading causes of death and for deaths from rare and reportable diseases in New York City from February 2015 through June 2020. We used results to find aberrations that indicate threats to public health. METHODS We used unobserved components models to analyze time series of mortality counts for leading causes of death, historical limits methods for rare and reportable diseases, and SaTScan for temporal-spatial cluster analysis. We obtained data on the number of deaths from the electronic death registry system maintained by the city's Bureau of Vital Statistics. RESULTS The mortality syndromic surveillance system detected an increase in the number of deaths from heart disease by April 1, 2020, when 75.0 deaths occurred on March 24, 2020, instead of an expected 45.8 deaths (95% upper prediction limit of 61.0) and an increase in the number of deaths from all causes on March 20, 2020, when 194.0 deaths were observed while 150.1 deaths were expected (95% upper prediction limit of 178.0). The number of deaths from all causes returned to normal the week beginning June 14, 2020, when 990.0 deaths were observed and 998.8 deaths were expected. PRACTICE IMPLICATIONS When compared with efforts from New York City to provide yearly vital statistics, the automated mortality syndromic surveillance system can provide timely mortality data with fewer resources and raise the capacity to detect anomalous increases in mortality.
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Affiliation(s)
- Alejandro F Castro
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Wenhui Li
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Blanca Bernard-Davila
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Mary Huynh
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Gretchen Van Wye
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
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Mahmud I, Kabir MM, Mridha MF, Alfarhood S, Safran M, Che D. Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel. Diagnostics (Basel) 2023; 13:2540. [PMID: 37568902 PMCID: PMC10417090 DOI: 10.3390/diagnostics13152540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
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Affiliation(s)
- Istiak Mahmud
- Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh;
| | - Md Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh;
| | - M. F. Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Dunren Che
- School of Computing, Southern Illinois University, Carbondale, IL 62901, USA;
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Magalhaes ES, Zhang D, Wang C, Thomas P, Moura CAA, Holtkamp DJ, Trevisan G, Rademacher C, Silva GS, Linhares DCL. Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System. Animals (Basel) 2023; 13:2412. [PMID: 37570221 PMCID: PMC10417698 DOI: 10.3390/ani13152412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.
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Affiliation(s)
- Edison S. Magalhaes
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
| | - Danyang Zhang
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA 50011, USA
| | - Chong Wang
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
- Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA 50011, USA
| | - Pete Thomas
- Iowa Select Farms, Iowa Falls, IA 50126, USA
| | | | - Derald J. Holtkamp
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
| | - Giovani Trevisan
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
| | - Christopher Rademacher
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
| | - Gustavo S. Silva
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
| | - Daniel C. L. Linhares
- Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA
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Edianto A, Trencher G, Manych N, Matsubae K. Forecasting coal power plant retirement ages and lock-in with random forest regression. Patterns (N Y) 2023; 4:100776. [PMID: 37521043 PMCID: PMC10382988 DOI: 10.1016/j.patter.2023.100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 03/24/2023] [Accepted: 05/19/2023] [Indexed: 08/01/2023]
Abstract
Averting dangerous climate change requires expediting the retirement of coal-fired power plants (CFPPs). Given multiple barriers hampering this, here we forecast the future retirement ages of the world's CFPPs. We use supervised machine learning to first learn from the past, determining the factors that influenced historical retirements. We then apply our model to a dataset of 6,541 operating or under-construction units in 66 countries. Based on results, we also forecast associated carbon emissions and the degree to which countries are locked in to coal power. Contrasting with the historical average of roughly 40 years over 2010-2021, our model forecasts earlier retirement for 63% of current CFPP units. This results in 38% less emissions than if assuming historical retirement trends. However, the lock-in index forecasts considerable difficulties to retire CFPPs early in countries with high dependence on coal power, a large capacity or number of units, and young plant ages.
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Affiliation(s)
- Achmed Edianto
- Graduate School of Environmental Studies, Tohoku University, Miyagi, Japan
| | - Gregory Trencher
- Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan
| | - Niccolò Manych
- Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany
- Department Economics of Climate Change, Technische Universität Berlin, Berlin, Germany
| | - Kazuyo Matsubae
- Graduate School of Environmental Studies, Tohoku University, Miyagi, Japan
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Diao O, Absil PA, Diallo M. Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal. Int J Environ Res Public Health 2023; 20:6303. [PMID: 37444150 PMCID: PMC10341430 DOI: 10.3390/ijerph20136303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 06/29/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-𝓁j, where t is the observation time and 𝓁j is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.
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Affiliation(s)
- Ousmane Diao
- ICTEAM Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium;
| | - P.-A. Absil
- ICTEAM Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium;
| | - Mouhamadou Diallo
- Molecular Biology Unit/Bacteriology-Virology Lab, CNHU A. Le Dantec/Université Cheikh Anta Diop, Dakar Fann P.O. Box 5005, Senegal;
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Mussina K, Kadyrov S, Kashkynbayev A, Yerdessov S, Zhakhina G, Sakko Y, Zollanvari A, Gaipov A. Prevalence of HIV in Kazakhstan 2010-2020 and Its Forecasting for the Next 10 Years. HIV AIDS (Auckl) 2023; 15:387-397. [PMID: 37426767 PMCID: PMC10329475 DOI: 10.2147/hiv.s413876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
Background HIV is a growing public health burden that threatens thousands of people in Kazakhstan. Countries around the world, including Kazakhstan, are facing significant problems in predicting HIV infection prevalence. It is crucial to understand the epidemiological trends of infectious diseases and to monitor the prevalence of HIV in a long-term perspective. Thus, in this study, we aimed to forecast the prevalence of HIV in Kazakhstan for 10 years from 2020 to 2030 by using mathematical modeling and time series analysis. Methods We use statistical Autoregressive Integrated Moving Average (ARIMA) models and a nonlinear epidemic Susceptible-Infected (SI) model to forecast the HIV infection prevalence rate in Kazakhstan. We estimated the parameters of the models using open data on the prevalence of HIV infection among women and men (aged 15-49 years) in Kazakhstan provided by the Kazakhstan Bureau of National Statistics. We also predict the effect of pre-exposure prophylaxis (PrEP) control measures on the prevalence rate. Results The ARIMA (1,2,0) model suggests that the prevalence of HIV infection in Kazakhstan will increase from 0.29 in 2021 to 0.47 by 2030. On the other hand, the SI model suggests that this parameter will increase to 0.60 by 2030 based on the same data. Both models were statistically significant by Akaike Information Criterion corrected (AICc) score and by the goodness of fit. HIV prevention under the PrEP strategy on the SI model showed a significant effect on the reduction of the HIV prevalence rate. Conclusion This study revealed that ARIMA (1,2,0) predicts a linear increasing trend, while SI forecasts a nonlinear increase with a higher prevalence of HIV. Therefore, it is recommended for healthcare providers and policymakers use this model to calculate the cost required for the regional allocation of healthcare resources. Moreover, this model can be used for planning effective healthcare treatments.
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Affiliation(s)
- Kamilla Mussina
- Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan
| | - Shirali Kadyrov
- Department of Mathematics and Natural Sciences, Suleyman Demirel University, Kaskelen, Kazakhstan
| | | | - Sauran Yerdessov
- Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan
| | - Gulnur Zhakhina
- Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan
| | - Yesbolat Sakko
- Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan
| | - Amin Zollanvari
- Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
| | - Abduzhappar Gaipov
- Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan
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Glass GE. Forecasting Outbreaks of Hantaviral Disease: Future Directions in Geospatial Modeling. Viruses 2023; 15:1461. [PMID: 37515149 PMCID: PMC10383283 DOI: 10.3390/v15071461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.
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