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Simoncini E, Jarry A, Moussion A, Marcheschi A, Giordanino P, Lusenti C, Bruder N, Velly L, Boussen S. Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study. Comput Inform Nurs 2024; 42:457-462. [PMID: 38252546 DOI: 10.1097/cin.0000000000001100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.
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Affiliation(s)
- Elsa Simoncini
- Author Affiliations: Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université (Ms Simoncini, Mrs Jarry, Mrs Moussion, Ms Marcheschi, Mrs Giordanino, Ms Lusenti, and Drs Bruder, Velly, and Boussen); Aix Marseille Université, IFSTTAR, LBA UMR_T 24 (Dr Boussen); and Institut des Neurociences de la Timone, CNRS UMR1106, Faculté de Médecine, Aix-Marseille Université (Dr Velly), France
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Kuziemsky CE, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024; 26:e54705. [PMID: 38776538 PMCID: PMC11153979 DOI: 10.2196/54705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
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Affiliation(s)
| | - Dillon Chrimes
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Simon Minshall
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | | | - Francis Lau
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
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Sadeghi S, Hempel L, Rodemund N, Kirsten T. Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV. Sci Rep 2024; 14:11438. [PMID: 38763952 PMCID: PMC11102905 DOI: 10.1038/s41598-024-61380-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/06/2024] [Indexed: 05/21/2024] Open
Abstract
The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.
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Affiliation(s)
- Sina Sadeghi
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
| | - Lars Hempel
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- Faculty Applied Computer and Bio Sciences, Mittweida University of Applied Sciences, Mittweida, Germany
| | - Niklas Rodemund
- Department of Anaesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Toralf Kirsten
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- Faculty Applied Computer and Bio Sciences, Mittweida University of Applied Sciences, Mittweida, Germany
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Tariq MU, Ismail SB. AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health Res Perspect 2024; 15:115-136. [PMID: 38621765 PMCID: PMC11082441 DOI: 10.24171/j.phrp.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
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Affiliation(s)
- Muhammad Usman Tariq
- Marketing, Operations, and Information System, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
| | - Shuhaida Binti Ismail
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
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Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. J Clin Med 2024; 13:1837. [PMID: 38610602 PMCID: PMC11013017 DOI: 10.3390/jcm13071837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Andrés Bas
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Santiago Herrero
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Pilar Blasco
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Miguel Suárez
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Department of Gastroenterology, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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Indolfi C, Klain A, Dinardo G, D’Addio E, Ferrara S, Decimo F, Ciprandi G, Tosca MA, Miraglia del Giudice M. COVID-19 Pediatric Follow-Up: Respiratory Long COVID-Associated Comorbidities and Lung Ultrasound Alterations in a Cohort of Italian Children. CHILDREN (BASEL, SWITZERLAND) 2024; 11:166. [PMID: 38397278 PMCID: PMC10887372 DOI: 10.3390/children11020166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
In children, the factors that influence COVID-19 disease and its medium- and long-term effects are little known. Our investigation sought to evaluate the presence of comorbidity factors associated with respiratory long COVID manifestations in children and to study ultrasound abnormalities following SARS-CoV-2 infection. Children, who arrived at the 'Respiratory Diseases of Pediatric Interest Unit' at the Department of Woman, Child, and General and Specialized Surgery of the University of Campania 'Luigi Vanvitelli', were selected during the timeframe from September 2021 to October 2022. The children were diagnosed with a SARS-CoV-2 infection that occurred at least one month before the visit. All patients followed a COVID-19 follow-up protocol, developed by the Italian Society of Pediatric Respiratory Diseases (SIMRI), which included: collection of data regarding SARS-CoV-2 illness and history of known respiratory and allergic diseases; physical examination; BMI assessment; baseline spirometry and after bronchodilation test; six-minute walking test; and lung ultrasound (LUS). In a cohort of 104 participants with respiratory long COVID symptoms (64.7% male, average age 8.92 years), 46.1% had fever with other symptoms, and 1% required hospitalization. BMI analysis showed 58.4% of the cohort was overweight. The LUS was positive in 27.0% of cases. A significant BMI association was observed with COVID-19 symptoms and LUS score (p-value < 0.05). No associations were found with asthma or atopy.
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Affiliation(s)
- Cristiana Indolfi
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Angela Klain
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Giulio Dinardo
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Elisabetta D’Addio
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Serena Ferrara
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Fabio Decimo
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
| | - Giorgio Ciprandi
- Department of Medicine, Allergy Clinic, Casa di Cura Villa Montallegro, 16145 Genoa, Italy;
| | - Maria Angela Tosca
- Pediatric Allergy Center, Istituto Giannina Gaslini, 16147 Genoa, Italy;
| | - Michele Miraglia del Giudice
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy; (C.I.); (G.D.); (E.D.); (S.F.); (F.D.); (M.M.d.G.)
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Shiferaw KB, Wali P, Waltemath D, Zeleke AA. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling. Front Cardiovasc Med 2024; 10:1308668. [PMID: 38235288 PMCID: PMC10793658 DOI: 10.3389/fcvm.2023.1308668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/04/2023] [Indexed: 01/19/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.
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Affiliation(s)
- Kirubel Biruk Shiferaw
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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Rakhshan SA, Zaj M, Ghane FH, Nejad MS. Exploring the potential of learning methods and recurrent dynamic model with vaccination: A comparative case study of COVID-19 in Austria, Brazil, and China. Phys Rev E 2024; 109:014212. [PMID: 38366403 DOI: 10.1103/physreve.109.014212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 12/11/2023] [Indexed: 02/18/2024]
Abstract
In order to effectively manage infectious diseases, it is crucial to understand the interplay between disease dynamics and human conduct. Various factors can impact the control of an epidemic, including social interventions, adherence to health protocols, mask-wearing, and vaccination. This article presents the development of an innovative hybrid model, known as the Combined Dynamic-Learning Model, that integrates classical recurrent dynamic models with four different learning methods. The model is composed of two approaches: The first approach introduces a traditional dynamic model that focuses on analyzing the impact of vaccination on the occurrence of an epidemic, and the second approach employs various learning methods to forecast the potential outcomes of an epidemic. Furthermore, our numerical results offer an interesting comparison between the traditional approach and modern learning techniques. Our classic dynamic model is a compartmental model that aims to analyze and forecast the diffusion of epidemics. The model we propose has a recurrent structure with piecewise constant parameters and includes compartments for susceptible, exposed, vaccinated, infected, and recovered individuals. This model can accurately mirror the dynamics of infectious diseases, which enables us to evaluate the impact of restrictive measures on the spread of diseases. We conduct a comprehensive dynamic analysis of our model. Additionally, we suggest an optimal numerical design to determine the parameters of the system. Also, we use regression tree learning, bidirectional long short-term memory, gated recurrent unit, and a combined deep learning method for training and evaluation of an epidemic. In the final section of our paper, we apply these methods to recently published data on COVID-19 in Austria, Brazil, and China from 26 February 2021 to 4 August 2021, which is when vaccination efforts began. To evaluate the numerical results, we utilized various metrics such as RMSE and R-squared. Our findings suggest that the dynamic model is ideal for long-term analysis, data fitting, and identifying parameters that impact epidemics. However, it is not as effective as the supervised learning method for making long-term forecasts. On the other hand, supervised learning techniques, compared to dynamic models, are more effective for predicting the spread of diseases, but not for analyzing the behavior of epidemics.
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Affiliation(s)
- Seyed Ali Rakhshan
- Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marzie Zaj
- Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Mahdi Soltani Nejad
- Department of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Djeddi WE, Hermi K, Ben Yahia S, Diallo G. Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. BMC Bioinformatics 2023; 24:488. [PMID: 38114937 PMCID: PMC10731821 DOI: 10.1186/s12859-023-05593-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery. RESULTS The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets. CONCLUSIONS The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.
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Affiliation(s)
- Warith Eddine Djeddi
- LR11ES14, Faculty of Sciences of Tunis, University of Tunis El Manar, Campus Universitaire, 2092, Tunis, Tunisia.
- High Institute of Informatics in Kef, University of Jendouba, Saleh Ayech, 8189, Jendouba, Tunisia.
| | - Khalil Hermi
- High Institute of Informatics in Kef, University of Jendouba, Saleh Ayech, 8189, Jendouba, Tunisia
| | - Sadok Ben Yahia
- Department of Software Science, Tallinn University of Technology, Ehitajate tee-5, 12618, Tallinn, Estonia
- The Maersk Mc-Kinney Moller Institute, Southern Syddansk Universitet, Alsion 2, 6400, Sønderborg, Denmark
| | - Gayo Diallo
- Bordeaux Population Health Inserm 1219, University of Bordeaux, rue Léo Saignat, 33000, Bordeaux, France
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Sultan LR, Haertter A, Al-Hasani M, Demiris G, Cary TW, Tung-Chen Y, Sehgal CM. Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound. AI 2023; 4:875-887. [PMID: 37929255 PMCID: PMC10623579 DOI: 10.3390/ai4040044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023] Open
Abstract
With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.
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Affiliation(s)
- Laith R. Sultan
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Allison Haertter
- Radiation Oncology Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maryam Al-Hasani
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - George Demiris
- Informatics Division of the Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore W. Cary
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Yale Tung-Chen
- Emergency Medicine Department, La Madrida Hospital, 28006 Madrid, Spain
| | - Chandra M. Sehgal
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
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12
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Casillas N, Ramón A, Torres AM, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023; 15:2184. [PMID: 38005862 PMCID: PMC10675561 DOI: 10.3390/v15112184] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/21/2023] [Accepted: 10/28/2023] [Indexed: 11/26/2023] Open
Abstract
The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.
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Affiliation(s)
- Nazaret Casillas
- Department of Internal Medicine, Hospital Virgen De La Luz, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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13
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Zapata RD, Huang S, Morris E, Wang C, Harle C, Magoc T, Mardini M, Loftus T, Modave F. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records. PLoS One 2023; 18:e0292888. [PMID: 37862334 PMCID: PMC10588875 DOI: 10.1371/journal.pone.0292888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 09/30/2023] [Indexed: 10/22/2023] Open
Abstract
OBJECTIVE This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home. METHODS We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition. RESULTS We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities. SIGNIFICANCE This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.
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Affiliation(s)
- Ruben D. Zapata
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Shu Huang
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States of America
| | - Earl Morris
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, FL, United States of America
| | - Chang Wang
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Christopher Harle
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL, United States of America
| | - Tanja Magoc
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL, United States of America
| | - Mamoun Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Tyler Loftus
- Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - François Modave
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States of America
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, United States of America
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14
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McFadden BR, Reynolds M, Inglis TJJ. Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice. Front Digit Health 2023; 5:1260602. [PMID: 37829595 PMCID: PMC10565494 DOI: 10.3389/fdgth.2023.1260602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/15/2023] [Indexed: 10/14/2023] Open
Abstract
Infection science is a discipline of healthcare which includes clinical microbiology, public health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures. The importance of infection science has become more apparent in recent years during the SARS-CoV-2 (COVID-19) pandemic and subsequent highlighting of critical operational domains within infection science including the hospital, clinical laboratory, and public health environments to prevent, manage, and treat infectious diseases. However, as the global community transitions beyond the pandemic, the importance of infection science remains, with emerging infectious diseases, bloodstream infections, sepsis, and antimicrobial resistance becoming increasingly significant contributions to the burden of global disease. Machine learning (ML) is frequently applied in healthcare and medical domains, with growing interest in the application of ML techniques to problems in infection science. This has the potential to address several key aspects including improving patient outcomes, optimising workflows in the clinical laboratory, and supporting the management of public health. However, despite promising results, the implementation of ML into clinical practice and workflows is limited. Enabling the migration of ML models from the research to real world environment requires the development of trustworthy ML systems that support the requirements of users, stakeholders, and regulatory agencies. This paper will provide readers with a brief introduction to infection science, outline the principles of trustworthy ML systems, provide examples of the application of these principles in infection science, and propose future directions for moving towards the development of trustworthy ML systems in infection science.
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Affiliation(s)
- Benjamin R. McFadden
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Mark Reynolds
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Timothy J. J. Inglis
- Western Australian Country Health Service, Perth, WA, Australia
- School of Medicine, University of Western Australia, Perth, WA, Australia
- Department of Microbiology, Pathwest Laboratory Medicine, Perth, WA, Australia
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15
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Maiti P, Nand M, Mathpal S, Wahab S, Kuniyal JC, Sharma P, Joshi T, Ramakrishnan MA, Chandra S. Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening. J Biomol Struct Dyn 2023:1-14. [PMID: 37732349 DOI: 10.1080/07391102.2023.2257333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023]
Abstract
The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of -10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88-161.89 nm2), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Priyanka Maiti
- G.B. Pant National Institute of Himalayan Environment (NIHE), Almora, India
| | - Mahesha Nand
- G.B. Pant National Institute of Himalayan Environment (NIHE), Almora, India
| | - Shalini Mathpal
- Department of Biotechnology, Kumaun University, Nainital, India
| | - Shadma Wahab
- Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | | | - Priyanka Sharma
- Department of Botany, D.S.B. Campus, Kumaun University, Nainital, India
| | - Tushar Joshi
- Department of Biotechnology, Kumaun University, Nainital, India
| | | | - Subhash Chandra
- Department of Botany, Soban Singh Jeena University, Almora, India
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16
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Ghassemi N, Shoeibi A, Khodatars M, Heras J, Rahimi A, Zare A, Zhang YD, Pachori RB, Gorriz JM. Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Appl Soft Comput 2023; 144:110511. [PMID: 37346824 PMCID: PMC10263244 DOI: 10.1016/j.asoc.2023.110511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/23/2022] [Accepted: 06/08/2023] [Indexed: 06/23/2023]
Abstract
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.
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Affiliation(s)
- Navid Ghassemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Jonathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain
| | - Alireza Rahimi
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
| | - J Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
- Department of Psychiatry, University of Cambridge, UK
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17
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Sharifi-Kia A, Nahvijou A, Sheikhtaheri A. Machine learning-based mortality prediction models for smoker COVID-19 patients. BMC Med Inform Decis Mak 2023; 23:129. [PMID: 37479990 PMCID: PMC10360290 DOI: 10.1186/s12911-023-02237-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population. METHODS A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated. RESULTS The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F1 score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F1 score of 89.9%. Active smoking was among the most important features in patients' mortality prediction. CONCLUSION Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.
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Affiliation(s)
- Ali Sharifi-Kia
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azin Nahvijou
- Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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18
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Tariq MU, Ismail SB, Babar M, Ahmad A. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS One 2023; 18:e0287755. [PMID: 37471397 PMCID: PMC10359009 DOI: 10.1371/journal.pone.0287755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023] Open
Abstract
The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
| | | | - Muhammad Babar
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
| | - Ashir Ahmad
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
- Swinburne University of Technology, Melbourne, Australia
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19
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Dobrijević D, Vilotijević-Dautović G, Katanić J, Horvat M, Horvat Z, Pastor K. Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023; 15:1522. [PMID: 37515208 PMCID: PMC10383367 DOI: 10.3390/v15071522] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Gordana Vilotijević-Dautović
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jasmina Katanić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Mirjana Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Zoltan Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Kristian Pastor
- Faculty of Technology, University of Novi Sad, 21000 Novi Sad, Serbia
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20
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A simulation of undiagnosed population and excess mortality during the COVID-19 pandemic. RESULTS IN CONTROL AND OPTIMIZATION 2023; 12:100262. [PMCID: PMC10290741 DOI: 10.1016/j.rico.2023.100262] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 06/21/2024]
Abstract
Whereas the extent of outbreak of COVID-19 is usually accessed via the number of reported cases and the number of patients succumbed to the disease, the officially recorded overall excess mortality numbers during the pandemic waves, which are significant and often followed the rise and fall of the pandemic waves, put a question mark on the above methodology. Gradually it has been recognized that estimating the size of the undiagnosed population (which includes asymptomatic cases and symptomatic cases but not reported) is also crucial. Here we used the classical mathematical SEIR model having an additional compartment, that is the undiagnosed group in addition to the susceptible, exposed, diagnosed, recovered and deceased groups, to link the undiagnosed COVID-19 cases to the reported excess mortality numbers and thereby try to know the actual size of the disease outbreak. The developed model wase successfully applied to relevant COVID-19 waves in USA (initial months of 2020), South Africa (mid of 2021) and Russia (2020–21) when a large discrepancy between the reported COVID-19 mortality and the overall excess mortality had been noticed.
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21
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Mulenga C, Kaonga P, Hamoonga R, Mazaba ML, Chabala F, Musonda P. Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning. Glob Health Epidemiol Genom 2023; 2023:8921220. [PMID: 37260675 PMCID: PMC10228226 DOI: 10.1155/2023/8921220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/23/2023] [Accepted: 04/27/2023] [Indexed: 06/02/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients' hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings.
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Affiliation(s)
- Clyde Mulenga
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
- Institute of Basic and Biomedical Sciences, Levy Mwanawasa Medical University, Lusaka, Zambia
| | - Patrick Kaonga
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
| | - Raymond Hamoonga
- The Health Press, Zambia National Public Health Institute, Lusaka, Zambia
| | - Mazyanga Lucy Mazaba
- Communication Information and Research, Zambia National Public Health Institute, Lusaka, Zambia
| | - Freeman Chabala
- Institute of Basic and Biomedical Sciences, Levy Mwanawasa Medical University, Lusaka, Zambia
| | - Patrick Musonda
- Department of Epidemiology and Biostatistics, University of Zambia, Lusaka, Zambia
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22
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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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Rakhshan SA, Nejad MS, Zaj M, Ghane FH. Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19. Comput Biol Med 2023; 158:106817. [PMID: 36989749 PMCID: PMC10035804 DOI: 10.1016/j.compbiomed.2023.106817] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 03/25/2023]
Abstract
It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.
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Affiliation(s)
| | - Mahdi Soltani Nejad
- Department of Railway Engineering, Iran University of Science and Technology, Iran
| | - Marzie Zaj
- Department of Mathematics, Ferdowsi University of Mashhad, Iran
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Ceccon DM, Amaral PHR, Andrade LM, da Silva MIN, Andrade LAF, Moraes TFS, Bagno FF, Rocha RP, de Almeida Marques DP, Ferreira GM, Lourenço AA, Ribeiro ÁL, Coelho-dos-Reis JGA, da Fonseca FG, Gonzalez JC. New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning. Braz J Microbiol 2023:10.1007/s42770-023-00923-5. [PMID: 36854899 PMCID: PMC9974055 DOI: 10.1007/s42770-023-00923-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 02/07/2023] [Indexed: 03/02/2023] Open
Abstract
Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.
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Affiliation(s)
- Denny M. Ceccon
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Paulo Henrique R. Amaral
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Lídia M. Andrade
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Maria I. N. da Silva
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
| | - Luis A. F. Andrade
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Thais F. S. Moraes
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Flavia F. Bagno
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raissa P. Rocha
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Geovane Marques Ferreira
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Alice Aparecida Lourenço
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ágata Lopes Ribeiro
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Jordana G. A. Coelho-dos-Reis
- Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Flavio G. da Fonseca
- Centro de Tecnologia Em Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil ,Laboratório de Virologia Básica E Aplicada, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - J. C. Gonzalez
- Departamento de Física, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Campus Pampulha 31270-901, Belo Horizonte, Minas Gerais 6627 Brazil
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Ong V, Soleimani A, Amirghasemi F, Khazaee Nejad S, Abdelmonem M, Razaviyayn M, Hosseinzadeh P, Comai L, Mousavi MPS. Impedimetric Sensing: An Emerging Tool for Combating the COVID-19 Pandemic. BIOSENSORS 2023; 13:204. [PMID: 36831970 PMCID: PMC9953732 DOI: 10.3390/bios13020204] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 06/12/2023]
Abstract
The COVID-19 pandemic revealed a pressing need for the development of sensitive and low-cost point-of-care sensors for disease diagnosis. The current standard of care for COVID-19 is quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). This method is sensitive, but takes time, effort, and requires specialized equipment and reagents to be performed correctly. This make it unsuitable for widespread, rapid testing and causes poor individual and policy decision-making. Rapid antigen tests (RATs) are a widely used alternative that provide results quickly but have low sensitivity and are prone to false negatives, particularly in cases with lower viral burden. Electrochemical sensors have shown much promise in filling this technology gap, and impedance spectroscopy specifically has exciting potential in rapid screening of COVID-19. Due to the data-rich nature of impedance measurements performed at different frequencies, this method lends itself to machine-leaning (ML) algorithms for further data processing. This review summarizes the current state of impedance spectroscopy-based point-of-care sensors for the detection of the SARS-CoV-2 virus. This article also suggests future directions to address the technology's current limitations to move forward in this current pandemic and prepare for future outbreaks.
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Affiliation(s)
- Victor Ong
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Ali Soleimani
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Farbod Amirghasemi
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Sina Khazaee Nejad
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Mona Abdelmonem
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Meisam Razaviyayn
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Parisa Hosseinzadeh
- Knight Campus Center Department of Bioengineering, University of Oregon, Eugene, OR 97403, USA
| | - Lucio Comai
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Maral P. S. Mousavi
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
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26
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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27
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Ho ML, Arnold CW, Decker SJ, Hazle JD, Krupinski EA, Mankoff DA. Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic. Acad Radiol 2023; 30:631-639. [PMID: 36764883 PMCID: PMC9816088 DOI: 10.1016/j.acra.2022.12.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 01/09/2023]
Abstract
Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.
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Affiliation(s)
- Mai-Lan Ho
- Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio.
| | | | | | - John D. Hazle
- The University of Texas MD Anderson Cancer Center, Houston, Texas
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28
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Machado MAD, Silva RRE, Namias M, Lessa AS, Neves MCLC, Silva CTA, Oliveira DM, Reina TR, Lira AAB, Almeida LM, Zanchettin C, Netto EM. Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography. J Med Biol Eng 2023; 43:156-162. [PMID: 37077697 PMCID: PMC9990550 DOI: 10.1007/s40846-023-00781-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/16/2023] [Indexed: 04/21/2023]
Abstract
Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.
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Affiliation(s)
- Marcos A. D. Machado
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
| | - Ronnyldo R. E. Silva
- Radtec Serviços em Física Médica, Salvador, Bahia 40060-330 Brazil
- Department of Systems and Computing, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58429-900 Brazil
| | - Mauro Namias
- Department of Medical Physics, Nuclear Diagnostic Center Foundation, C1417CVE Buenos Aires, Argentina
| | - Andreia S. Lessa
- Department of Radiology, Hospital Universitário Gaffrée e Guinle, Universidade do Rio de Janeiro (UNIRIO), Rio de Janeiro, 20270-004 Brazil
| | - Margarida C. L. C. Neves
- Department of Pneumology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Carolina T. A. Silva
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
| | - Danillo M. Oliveira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
- Northeast Regional Nuclear Science Centre (CRCN-NE), Recife, Pernambuco 50840-545 Brazil
- Nuclear Energy Department, Universidade Federal de Pernambuco, Recife, Pernambuco 50740-540 Brazil
| | - Thamiris R. Reina
- Department of Radiology, Hospital Universitário da Universidade Federal de Juiz de Fora/ Ebserh, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais 36038-330 Brazil
| | - Arquimedes A. B. Lira
- Department of Radiology, Hospital Universitário Alcides Carneiro/ Ebserh, Universidade Federal de Campina Grande, Campina Grande, Paraíba 58400-398 Brazil
| | - Leandro M. Almeida
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
| | - Cleber Zanchettin
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Pernambuco 50720-001 Brazil
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208 USA
| | - Eduardo M. Netto
- Infectious Disease Research Laboratory, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia 40110-040 Brazil
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Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru. Int J Med Inform 2023; 169:104913. [PMID: 36410127 DOI: 10.1016/j.ijmedinf.2022.104913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Nowadays it is necessary to strengthen health information systems and data-based solutions. However, there are few graduate training programs in Peru to use tools and methods of data science applied in public health. This article describes the development process and the initial assessment regarding the experience of the participants in an international multidisciplinary diploma in data intelligence for pandemics and epidemics preparedness, which was carried out from January to May 2021. The diploma was structured in 7 modules and 40 Peruvian professionals participated, of which 11 (27.5%) were women, and 16 (40%) came from regions outside of Lima and Callao. We discussed the need to strengthen institutional and health professionals' capacity to adequately manage large volumes of data, information, and knowledge through the application of emerging technologies to optimize data management processes to improve decision-making in health.
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Wichmann RM, Fagundes TP, de Oliveira TA, Batista AFDM, Chiavegatto Filho ADP. Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. PLoS One 2022; 17:e0278397. [PMID: 36516134 PMCID: PMC9749966 DOI: 10.1371/journal.pone.0278397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 11/15/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.
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Affiliation(s)
- Roberta Moreira Wichmann
- School of Public Health, University of São Paulo, São Paulo, São Paulo, Brazil
- Brazilian Institute of Education, Development and Research – IDP, Economics Graduate Program, Brasilia, Federal District, Brazil
| | - Thales Pardini Fagundes
- Clinics Hospital of Ribeirão Preto of the University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | - André Filipe de Moraes Batista
- School of Public Health, University of São Paulo, São Paulo, São Paulo, Brazil
- Insper, Institute of Education and Research, São Paulo, São Paulo, Brazil
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Ramos MC, Gomes DF, Mello NFD, Silva END, Barreto JOM, Shimizu HE. Big Data e Inteligência Artificial para pesquisa translacional na Covid-19: revisão rápida. SAÚDE EM DEBATE 2022. [DOI: 10.1590/0103-1104202213518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
RESUMO O objetivo deste estudo foi identificar como a Inteligência Artificial (IA) vem sendo utilizada para a pesquisa translacional no contexto da Covid-19. Foi realizada uma revisão rápida para identificar o uso de técnicas de IA na translação de tecnologias para o enfrentamento da Covid-19. Empregou-se estratégia de busca com base em termos MeSH e seus respectivos sinônimos em sete bases de dados. Dos 59 artigos identificados, oito foram incluídos. Foram identificadas 11 experiências que usaram IA para a pesquisa translacional em Covid-19: predição de eficácia medicamentosa; predição de patogenicidade do Sars-CoV-2; diagnóstico de imagem para Covid-19; predição de incidência de Covid-19; estimativas de impacto da Covid-19 na sociedade; automatização de sanitização de ambientes hospitalares e clínicos; rastreio de pessoas infectadas e possivelmente infectadas; monitoramento do uso de máscaras; predição de gravidade de pacientes; estratificação de risco do paciente; e predição de recursos hospitalares. A pesquisa translacional pode ajudar no desenvolvimento produtivo e industrial em saúde, especialmente quando apoiada em métodos de IA, uma ferramenta cada vez mais importante, sobretudo quando se discute a Quarta Revolução Industrial e suas aplicações na saúde.
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De Groote H. Economic analysis of pest problems in agriculture and food chains in Africa. CURRENT OPINION IN INSECT SCIENCE 2022; 54:100969. [PMID: 36096416 DOI: 10.1016/j.cois.2022.100969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 06/15/2023]
Abstract
Economic impact of agricultural pests remains poorly studied, resulting in interventions rarely being evidence-based. When pests (re)emerge, researchers and donors often implement quick studies that tend to overestimate losses, leading to recommendations for immediate and substantial interventions. Incentives are lacking for scientists/research institutes to invest in objective pest assessments, as donors routinely fund projects without them. In this review, we argue for inclusion of social scientists and systematic economic analysis of pest problems and their control, consisting of four major steps: (1) estimating pests' extent and intensity, (2) conducting trials for technical and economical efficacy of control methods, (3) randomized controlled trials with participatory evaluation and effectiveness analysis of control options, and (4) impact assessment of pests and control methods. To optimize pest control, scientists and donors need to include socioeconomic analysis, leading to better use of research funds and reduction of negative environmental impacts.
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Affiliation(s)
- Hugo De Groote
- International Maize and Wheat Improvement Centre (CIMMYT), PO Box 1041-00621, Nairobi, Kenya.
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Using model explanations to guide deep learning models towards consistent explanations for EHR data. Sci Rep 2022; 12:19899. [PMID: 36400825 PMCID: PMC9674624 DOI: 10.1038/s41598-022-24356-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and finance, where transparency and explainability is paramount, this can be a significant barrier to DL adoption. In this study we present a further analysis of explanation (in)consistency on 6 tabular datasets/tasks, with a focus on Electronic Health Records data. We propose a novel deep learning ensemble architecture that trains its sub-models to produce consistent explanations, improving explanation consistency by as much as 315% (e.g. from 0.02433 to 0.1011 on MIMIC-IV), and on average by 124% (e.g. from 0.12282 to 0.4450 on the BCW dataset). We evaluate the effectiveness of our proposed technique and discuss the implications our results have for both industrial applications of DL and explainability as well as future methodological work.
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Kamalov F, Rajab K, Cherukuri AK, Elnagar A, Safaraliev M. Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing 2022; 511:142-154. [PMID: 36097509 PMCID: PMC9454152 DOI: 10.1016/j.neucom.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/03/2022] [Accepted: 09/04/2022] [Indexed: 11/21/2022]
Abstract
The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.
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Palermo MB, Policarpo LM, Costa CAD, Righi RDR. Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 11:40. [PMID: 36249862 PMCID: PMC9553296 DOI: 10.1007/s13721-022-00384-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
Abstract
This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.
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Affiliation(s)
- Marcelo Benedeti Palermo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Lucas Micol Policarpo
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Cristiano André da Costa
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
| | - Rodrigo da Rosa Righi
- Software Innovation Laboratory-SOFTWARELAB, Programa de Pós-Graduação em Computação Aplicada, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, São Leopoldo, RS 93022-750 Brazil
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Identification of Suitable Drug Combinations for Treating COVID-19 Using a Novel Machine Learning Approach: The RAIN Method. Life (Basel) 2022; 12:life12091456. [PMID: 36143492 PMCID: PMC9505329 DOI: 10.3390/life12091456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Abstract COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer.
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E. Samadi M, Kiefer S, Fritsch SJ, Bickenbach J, Schuppert A. A training strategy for hybrid models to break the curse of dimensionality. PLoS One 2022; 17:e0274569. [PMID: 36107916 PMCID: PMC9477345 DOI: 10.1371/journal.pone.0274569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/30/2022] [Indexed: 11/18/2022] Open
Abstract
Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task. Given a set of binary labeled data, the challenge is to use them to develop a model that accurately assesses labels of new unlabeled data. Our strategy employs graph-theoretic methods to analyze the data and deduce a function that maps input features to output labels. Our focus here is on data sets represented by binary features in which the label assessment of unlabeled data points is always extrapolation. Our strategy shows the existence of small sets of data points within given binary data for which knowing the labels allows for extrapolation to the entire valid input space. An implementation of our strategy yields a notable reduction of training-data demand in a binary classification task compared with different supervised machine learning algorithms. As an application, we have fitted a tree-structured hybrid model to the vital status of a cohort of COVID-19 patients requiring intensive-care unit treatment and mechanical ventilation. Our learning strategy yields the existence of patient cohorts for whom knowing the vital status enables extrapolation to the entire valid input space of the developed hybrid model.
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Affiliation(s)
- Moein E. Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
- Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
- * E-mail:
| | - Sandra Kiefer
- Max Planck Institute for Software Systems, Saarland Informatics Campus, Saarbrücken, Germany
| | - Sebastian Johaness Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
- Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
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Adadi A, Lahmer M, Nasiri S. Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2022; 34:5898-5920. [PMID: 37520766 PMCID: PMC8831917 DOI: 10.1016/j.jksuci.2021.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/21/2021] [Accepted: 07/11/2021] [Indexed: 12/15/2022]
Abstract
Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.
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Affiliation(s)
- Amina Adadi
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
| | - Mohammed Lahmer
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
| | - Samia Nasiri
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
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Ramón A, Zaragozá M, Torres AM, Cascón J, Blasco P, Milara J, Mateo J. Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab. J Clin Med 2022; 11:jcm11164729. [PMID: 36012968 PMCID: PMC9410189 DOI: 10.3390/jcm11164729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/05/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (SRF). The aim of our study was to provide evidence on predictors of poor outcome in patients with COVID-19 treated with tocilizumab, using machine learning (ML) techniques. We conducted a retrospective study, analyzing the clinical, laboratory and sociodemographic data of patients admitted for severe COVID-19 with SRF, treated with tocilizumab. The extreme gradient boost (XGB) method had the highest balanced accuracy (93.16%). The factors associated with a worse outcome of tocilizumab use in terms of mortality were: baseline situation at the start of tocilizumab treatment requiring invasive mechanical ventilation (IMV), elevated ferritin, lactate dehydrogenase (LDH) and glutamate-pyruvate transaminase (GPT), lymphopenia, and low PaFi [ratio between arterial oxygen pressure and inspired oxygen fraction (PaO2/FiO2)] values. The factors associated with a worse outcome of tocilizumab use in terms of hospital stay were: baseline situation at the start of tocilizumab treatment requiring IMV or supplemental oxygen, elevated levels of ferritin, glutamate-oxaloacetate transaminase (GOT), GPT, C-reactive protein (CRP), LDH, lymphopenia, and low PaFi values. In our study focused on patients with severe COVID-19 treated with tocilizumab, the factors that were weighted most strongly in predicting worse clinical outcome were baseline status at the start of tocilizumab treatment requiring IMV and hyperferritinemia.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Marta Zaragozá
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Joaquín Cascón
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Javier Milara
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- Centre for Biomedical Research Network on Respiratory Diseases (CIBERES), Health Institute Carlos III, 28029 Madrid, Spain
- Correspondence:
| | - Jorge Mateo
- Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
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Ramón A, Torres AM, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. J Investig Med 2022; 70:jim-2021-002278. [PMID: 35850970 DOI: 10.1136/jim-2021-002278] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 01/08/2023]
Abstract
Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.
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Affiliation(s)
- Antonio Ramón
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
| | - Ana Maria Torres
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Javier Milara
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
- Pharmacy Department, University of Valencia, Valencia, Spain
| | - Joaquín Cascón
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Pilar Blasco
- Pharmacy Department, General University Hospital Consortium of Valencia, Valencia, Spain
| | - Jorge Mateo
- Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
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Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model. Med Biol Eng Comput 2022; 60:2039-2049. [PMID: 35538201 PMCID: PMC9090454 DOI: 10.1007/s11517-022-02549-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 03/06/2022] [Indexed: 10/31/2022]
Abstract
Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the USA. The purpose of this study was to estimate the likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data. Patient records were extracted for all COVID-19 nasal swab PCR tests performed within the Providence St. Joseph Health system from February to October of 2020. A total of 316,599 participants were included in this study, and approximately 7.7% (n = 24,358) tested positive for COVID-19. A gradient boosting model, LightGBM (LGBM), predicted risk of initial infection with an area under the receiver operating characteristic curve of 0.819. Factors that predicted infection were cough, fever, being a member of the Hispanic or Latino community, being Spanish speaking, having a history of diabetes or dementia, and living in a neighborhood with housing insecurity. A model trained on sociodemographic, environmental, and medical history data performed well in predicting risk of a positive COVID-19 test. This model could be used to tailor education, public health policy, and resources for communities that are at the greatest risk of infection.
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Ormeño P, Márquez G, Guerrero-Nancuante C, Taramasco C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138058. [PMID: 35805713 PMCID: PMC9265284 DOI: 10.3390/ijerph19138058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022]
Abstract
Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F1-score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F1-score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.
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Affiliation(s)
- Pablo Ormeño
- Escuela de Ingenieria y Negocios, Universidad de Viña del Mar, Viña del Mar 2520000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Gastón Márquez
- Departamento de Electrónica e Informática, Universidad Técnica Federico Santa María, Millennium Nucleus on Sociomedicine, Concepción 4030000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Camilo Guerrero-Nancuante
- Escuela de Enfermería, Universidad de Valparaíso, Valparaíso 2500000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Carla Taramasco
- Facultad de Ingeniería, Universidad Andrés Bello, Millennium Nucleus on Sociomedicine, Viña del Mar 2520000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
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Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1644910. [PMID: 35756093 PMCID: PMC9226971 DOI: 10.1155/2022/1644910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/17/2022] [Accepted: 05/22/2022] [Indexed: 12/13/2022]
Abstract
Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.
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de Lara-Tuprio E, Estadilla CDS, Macalalag JMR, Teng TR, Uyheng J, Espina KE, Pulmano CE, Estuar MRJE, Sarmiento RFR. Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines. Epidemics 2022; 40:100599. [PMID: 35763978 PMCID: PMC9212903 DOI: 10.1016/j.epidem.2022.100599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 05/19/2022] [Accepted: 06/14/2022] [Indexed: 11/03/2022] Open
Abstract
Around the world, disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis, modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper, we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform, the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national, regional, and provincial levels guided government actions; and conversely, how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic, simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories (r=94%-99%, p<.001). Model simulations were subsequently utilized to predict the outcomes of proposed interventions, including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized, flexible, and responsive mathematical modeling, as applied to pandemic intelligence and for data-driven policy-making in general.
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Affiliation(s)
| | | | | | | | - Joshua Uyheng
- Department of Psychology, Ateneo de Manila University, Philippines.
| | - Kennedy E Espina
- Department of Information Systems and Computer Science, Ateneo de Manila University, Philippines
| | - Christian E Pulmano
- Department of Information Systems and Computer Science, Ateneo de Manila University, Philippines
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45
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Nicora G, Marini S, Salemi M, Bellazzi R. Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning. Stud Health Technol Inform 2022; 294:654-658. [PMID: 35612170 PMCID: PMC11093215 DOI: 10.3233/shti220550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this work we show that Incremental Machine Learning can be used to predict the classification of emerging SARS-CoV-2 lineages, dynamically distinguishing between neutral variants and non-neutral ones, i.e. variants of interest or variants of concerns. Starting from the Spike protein primary sequences collected in the GISAID db, we have derived a set of k-mers features, i.e., aminoacid subsequences with fixed length k. We have then implemented a Logistic Regression Incremental Learner that was monthly tested on the variants collected since February 2020 until October 2021. The average value of balanced accuracy of the classifier is 0.72 ± 0.2, which increased to 0.78 ± 0.16 in the last 12 months. The alpha, beta, gamma, eta, kappa and delta variants were recognized as non-neutral variants with mean recall ∼90%. In summary, incremental learning proved to be a useful instrument for pandemic surveillance, given its capability to update the model on new data over time.
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Affiliation(s)
- Giovanna Nicora
- Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
- enGenome S.r.l, Via Ferrata 5, Pavia, Italy
| | - Simone Marini
- Dept. of Epidemiology, University of Florida, Gainesville (FL)
| | - Marco Salemi
- Dept. of Pathology, University of Florida, Gainesville (FL)
| | - Riccardo Bellazzi
- Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
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Song W, Zhang L, Liu L, Sainlaire M, Karvar M, Kang MJ, Pullman A, Lipsitz S, Massaro A, Patil N, Jasuja R, Dykes PC. Predicting Hospitalization of COVID-19 Positive Patients Using Clinician-guided Machine Learning Methods. J Am Med Inform Assoc 2022; 29:1661-1667. [PMID: 35595237 PMCID: PMC9129151 DOI: 10.1093/jamia/ocac083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/11/2022] [Accepted: 05/13/2022] [Indexed: 11/18/2022] Open
Abstract
Objectives The coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19. Methods We screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network. Results All 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults. Conclusions In this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.
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Affiliation(s)
- Wenyu Song
- Department of Medicine, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Luwei Liu
- Department of Medicine, Brigham & Women's Hospital
| | | | - Mehran Karvar
- Department of Surgery, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Min-Jeoung Kang
- College of Nursing, The Catholic University of Korea, Seoul, South Korea
| | | | - Stuart Lipsitz
- Department of Medicine, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Anthony Massaro
- Department of Medicine, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Namrata Patil
- Department of Surgery, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Ravi Jasuja
- Department of Medicine, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
| | - Patricia C Dykes
- Department of Medicine, Brigham & Women's Hospital.,Harvard Medical School, Boston, MA
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Jemioło P, Storman D, Orzechowski P. Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review. J Clin Med 2022; 11:2054. [PMID: 35407664 PMCID: PMC9000039 DOI: 10.3390/jcm11072054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0-45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.
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Affiliation(s)
- Paweł Jemioło
- AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, al. A. Mickiewicza 30, 30-059 Krakow, Poland;
| | - Dawid Storman
- Chair of Epidemiology and Preventive Medicine, Department of Hygiene and Dietetics, Jagiellonian University Medical College, ul. M. Kopernika 7, 31-034 Krakow, Poland;
| | - Patryk Orzechowski
- AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, al. A. Mickiewicza 30, 30-059 Krakow, Poland;
- Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
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Saadatmand S, Salimifard K, Mohammadi R, Marzban M, Naghibzadeh-Tahami A. Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning. Med Biol Eng Comput 2022; 60:957-968. [PMID: 35147843 PMCID: PMC8832434 DOI: 10.1007/s11517-022-02519-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/01/2022] [Indexed: 01/08/2023]
Abstract
Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient's background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.
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Affiliation(s)
- Sara Saadatmand
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, 75169 Bushehr, Iran
| | - Khodakaram Salimifard
- Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, 75169 Bushehr, Iran
| | - Reza Mohammadi
- Department of Operation Management, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands
| | - Maryam Marzban
- Department of Public Health, School of Public Health, Bushehr University of Medical Science, Bushehr, Iran
| | - Ahmad Naghibzadeh-Tahami
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
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Hatmal MM, Al-Hatamleh MAI, Olaimat AN, Mohamud R, Fawaz M, Kateeb ET, Alkhairy OK, Tayyem R, Lounis M, Al-Raeei M, Dana RK, Al-Ameer HJ, Taha MO, Bindayna KM. Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors. Vaccines (Basel) 2022; 10:366. [PMID: 35334998 PMCID: PMC8955470 DOI: 10.3390/vaccines10030366] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.
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Affiliation(s)
- Ma’mon M. Hatmal
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
| | - Mohammad A. I. Al-Hatamleh
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia; (M.A.I.A.-H.); (R.M.)
| | - Amin N. Olaimat
- Department of Clinical Nutrition and Dietetics, Faculty of Applied Medical Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan;
| | - Rohimah Mohamud
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia; (M.A.I.A.-H.); (R.M.)
| | - Mirna Fawaz
- Nursing Department, Faculty of Health Sciences, Beirut Arab University, Beirut 1105, Lebanon;
| | - Elham T. Kateeb
- Oral Health Research and Promotion Unit, Faculty of Dentistry, Al-Quds University, Jerusalem 51000, Palestine;
| | - Omar K. Alkhairy
- Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh 11426, Saudi Arabia;
- King Saud bin Abdulaziz University for Health Sciences, P.O. Box 3660, Riyadh 11481, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), P.O. Box 3660, Riyadh 11481, Saudi Arabia
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar;
| | - Mohamed Lounis
- Department of Agro-Veterinary Science, Faculty of Natural and Life Sciences, University of Ziane Achour, BP 3117, Djelfa 17000, Algeria;
| | - Marwan Al-Raeei
- Faculty of Sciences, Damascus University, Damascus P.O. Box 30621, Syria;
| | - Rasheed K. Dana
- Faculty of Medicine, Mansoura University, Mansoura, Dakahlia 35516, Egypt;
| | - Hamzeh J. Al-Ameer
- Department of Biology and Biotechnology, Faculty of Science, American University of Madaba, P.O. Box 99, Madaba 17110, Jordan;
| | - Mutasem O. Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman 11942, Jordan;
| | - Khalid M. Bindayna
- Department of Microbiology, Immunology and Infectious Diseases, College of Medicine and Medical Sciences, Arabian Gulf University, Manama 329, Bahrain
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Kini AS, Gopal Reddy AN, Kaur M, Satheesh S, Singh J, Martinetz T, Alshazly H. Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7377502. [PMID: 35280708 PMCID: PMC8896964 DOI: 10.1155/2022/7377502] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022]
Abstract
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
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Affiliation(s)
- Anita S. Kini
- Manipal Institute of Technology MAHE, Manipal, Karnataka 576104, India
| | - A. Nanda Gopal Reddy
- Department of IT, Mahaveer Institute of Science and Technology, Hyderabad, Telangana 500005, India
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - S. Satheesh
- Department of Electronics and Communication Engineering, Malineni Lakshmaiah Women's Engineering College, Guntur, Andhra Pradesh 522017, India
| | - Jagendra Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida-203206, India
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck 23562, Germany
| | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
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