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Coiro S, Lacomblez C, Duarte K, Gargani L, Rastogi T, Chouihed T, Girerd N. A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure. Intern Emerg Med 2024:10.1007/s11739-024-03627-2. [PMID: 38780749 DOI: 10.1007/s11739-024-03627-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/23/2024] [Indexed: 05/25/2024]
Abstract
Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at developing a lung ultrasound-based model for AHF diagnosis using machine learning. Random forest and decision trees were generated using the LUS data (via an 8-zone scanning protocol) in patients with acute dyspnea admitted to the Emergency Department (PLUME study, N = 117) and subsequently validated in an external dataset (80 controls from the REMI study, 50 cases from the Nancy AHF cohort). Using the random forest model, total B-line sum (i.e., in both hemithoraces) was the most significant variable for identifying AHF, followed by the difference in B-line sum between the superior and inferior lung areas. The decision tree algorithm had a good diagnostic accuracy [area under the curve (AUC) = 0.865] and identified three risk groups (i.e., low 24%, high 70%, and very high-risk 96%) for AHF. The very high-risk group was defined by the presence of 14 or more B-lines in both hemithoraces while the high-risk group was described as having either B-lines mostly localized in superior points or in the right hemithorax. Accuracy in the validation cohort was excellent (AUC = 0.906). Importantly, adding the algorithm on top of a validated clinical score and classical definition of positive LUS scanning for AHF resulted in a significant improvement in diagnostic accuracy (continuous net reclassification improvement = 1.21, P < 0.001). Our simple lung ultrasound-based machine learning algorithm features an excellent performance and may constitute a validated strategy to diagnose AHF.
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Affiliation(s)
- Stefano Coiro
- Cardiology Department, Santa Maria Della Misericordia Hospital, Perugia, Italy
- Université de Lorraine, Centre D'Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Institut Lorrain du Coeur Et Des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre Lès Nancy, France
| | - Claire Lacomblez
- Université de Lorraine, Centre D'Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Institut Lorrain du Coeur Et Des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre Lès Nancy, France
| | - Kevin Duarte
- Université de Lorraine, Centre D'Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Institut Lorrain du Coeur Et Des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre Lès Nancy, France
| | - Luna Gargani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Tripti Rastogi
- Université de Lorraine, Centre D'Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Institut Lorrain du Coeur Et Des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre Lès Nancy, France
| | - Tahar Chouihed
- Emergency Department, INSERM, UMRS 1116, University Hospital of Nancy, Nancy, France
| | - Nicolas Girerd
- Université de Lorraine, Centre D'Investigation Clinique-Plurithématique Inserm CIC-P 1433, Inserm U1116, CHRU Nancy Hopitaux de Brabois, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Institut Lorrain du Coeur Et Des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre Lès Nancy, France.
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Irmak SE, Ozdemir GD, Ozdemir MA, Ercan UK. Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water. Biomed Phys Eng Express 2024; 10:045016. [PMID: 38697029 DOI: 10.1088/2057-1976/ad464f] [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: 02/07/2024] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.
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Affiliation(s)
- Seyma Ecem Irmak
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
| | - Gizem Dilara Ozdemir
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
| | - Mehmet Akif Ozdemir
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
| | - Utku Kürşat Ercan
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, 35620 Cigli, Izmir, Turkey
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Xu Z, Hu Y, Shao X, Shi T, Yang J, Wan Q, Liu Y. The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis. Cardiology 2024:1-19. [PMID: 38648752 DOI: 10.1159/000538639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/28/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis. METHODS PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods. RESULTS A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups. CONCLUSION The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.
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Affiliation(s)
- Zhaohui Xu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China,
| | - Yinqin Hu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xinyi Shao
- The Grier School, Tyrone, Pennsylvania, USA
| | - Tianyun Shi
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiahui Yang
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qiqi Wan
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yongming Liu
- Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Cardiovascular Disease, Anhui Provincial Hospital of Integrated Medicine, Hefei Anhui, China
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Fathima AJ, Fasla MMN. A comprehensive review on heart disease prognostication using different artificial intelligence algorithms. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38424704 DOI: 10.1080/10255842.2024.2319706] [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: 01/30/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis.
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Affiliation(s)
- A Jainul Fathima
- Assistant Professor, IT Francis Xavier Engineering College, Tirunelveli - 627003, India
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Sebastian SA, Co EL, Mahtani A, Padda I, Anam M, Mathew SS, Shahzadi A, Niazi M, Pawar S, Johal G. Heart Failure: Recent Advances and Breakthroughs. Dis Mon 2024; 70:101634. [PMID: 37704531 DOI: 10.1016/j.disamonth.2023.101634] [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] [Indexed: 09/15/2023]
Abstract
Heart failure (HF) is a common clinical condition encountered in various healthcare settings with a vast socioeconomic impact. Recent advancements in pharmacotherapy have led to the evolution of novel therapeutic agents with a decrease in hospitalization and mortality rates in HF with reduced left ventricular ejection fraction (HFrEF). Lately, the introduction of artificial intelligence (AI) to construct decision-making models for the early detection of HF has played a vital role in optimizing cardiovascular disease outcomes. In this review, we examine the newer therapies and evidence behind goal-directed medical therapy (GDMT) for managing HF. We also explore the application of AI and machine learning (ML) in HF, including early diagnosis and risk stratification for HFrEF.
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Affiliation(s)
| | - Edzel Lorraine Co
- University of Santo Tomas Faculty of Medicine and Surgery, Manila, Philippines
| | - Arun Mahtani
- Richmond University Medical Center/Mount Sinai, Staten Island, New York, USA
| | - Inderbir Padda
- Richmond University Medical Center/Mount Sinai, Staten Island, New York, USA
| | - Mahvish Anam
- Deccan College of Medical Sciences, Hyderabad, India
| | | | | | - Maha Niazi
- Royal Alexandra Hospital, Edmonton, Canada
| | | | - Gurpreet Johal
- Department of Cardiology, University of Washington, Valley Medical Center, Seattle, Washington, USA
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Nakao YM, Nadarajah R, Shuweihdi F, Nakao K, Fuat A, Moore J, Bates C, Wu J, Gale C. Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study. BMJ Open 2024; 14:e073455. [PMID: 38253453 PMCID: PMC10806764 DOI: 10.1136/bmjopen-2023-073455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/29/2023] [Indexed: 01/24/2024] Open
Abstract
INTRODUCTION Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF. METHODS AND ANALYSIS We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care. ETHICS AND DISSEMINATION Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION DETAILS The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).
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Affiliation(s)
- Yoko M Nakao
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan
| | - Ramesh Nadarajah
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospital NHS Trust, Leeds, UK
| | - Farag Shuweihdi
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Ahmet Fuat
- Carmel Medical Practice, Darlington & School of Medicine, Pharmacy and Health, Durham University, Darham, UK
| | - Jim Moore
- Stroke Road Surgery, Bishop's Cleeve, Cheltenham, UK
| | | | - Jianhua Wu
- Department of Biostatistics and Health Data Science, Queen Mary University of London, London, UK
| | - Chris Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospital NHS Trust, Leeds, UK
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Courville E, Kazim SF, Vellek J, Tarawneh O, Stack J, Roster K, Roy J, Schmidt M, Bowers C. Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis. Surg Neurol Int 2023; 14:262. [PMID: 37560584 PMCID: PMC10408617 DOI: 10.25259/sni_312_2023] [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: 04/08/2023] [Accepted: 06/21/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes. METHODS We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies. RESULTS ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms. CONCLUSION ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.
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Affiliation(s)
- Evan Courville
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - Syed Faraz Kazim
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - John Vellek
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Omar Tarawneh
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Julia Stack
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Katie Roster
- Department of Neurosurgery, School of Medicine, New York Medical College, Valhalla, New York, United States
| | - Joanna Roy
- Department of Neurosurgery, Topiwala National Medical and B. Y. L. Nair Charitable Hospital, Mumbai, Maharashtra, India
| | - Meic Schmidt
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
| | - Christian Bowers
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, United States
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Huang Y, Wang M, Zheng Z, Ma M, Fei X, Wei L, Chen H. Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients. J Biomed Inform 2023; 143:104427. [PMID: 37339714 DOI: 10.1016/j.jbi.2023.104427] [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: 01/06/2023] [Revised: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 06/22/2023]
Abstract
OBJECTIVE To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model, and further to predict the kidney failure (KF) status and mortality of heart failure (HF) patients. MATERIALS AND METHODS The time-invariant EMR data included demographic information and comorbidities, and the time-varying EMR data were lab tests. We used a Transformer encoder module to represent the time-invariant data, and refined a long short-term memory (LSTM) with a Transformer encoder attached to the top to represent the time-varying data, taking the original measured values and their corresponding embedding vectors, masking vectors, and two types of time intervals as inputs. The proposed representations of patients with time-invariant and time-varying data were used to predict KF status (949 out of 5268 HF patients diagnosed with KF) and mortality (463 in-hospital deaths) for HF patients. Comparative experiments were conducted between the proposed model and some representative machine learning models. Ablation experiments were also performed around the time-varying data representation, including replacing the refined LSTM with the standard LSTM, GRU-D and T-LSTM, respectively, and removing the Transformer encoder and the time-varying data representation module, respectively. The visualization of the attention weights of the time-invariant and time-varying features was used to clinically interpret the predictive performance. We used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the F1-score to evaluate the predictive performance of the models. RESULTS The proposed model achieved superior performance, with average AUROCs, AUPRCs and F1-scores of 0.960, 0.610 and 0.759 for KF prediction and 0.937, 0.353 and 0.537 for mortality prediction, respectively. Predictive performance improved with the addition of time-varying data from longer time periods. The proposed model outperformed the comparison and ablation references in both prediction tasks. CONCLUSIONS Both time-invariant and time-varying EMR data of patients could be efficiently represented by the proposed unified deep learning model, which shows higher performance in clinical prediction tasks. The way to use time-varying data in the current study is hopeful to be used in other kinds of time-varying data and other clinical tasks.
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Affiliation(s)
- Yanqun Huang
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Muyu Wang
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Zhimin Zheng
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Moxuan Ma
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
| | - Xiaolu Fei
- Information Center, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing 100053, China.
| | - Lan Wei
- Information Center, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing 100053, China.
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
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Li X, Dai A, Tran R, Wang J. Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective. Breast Cancer Res Treat 2023:10.1007/s10549-023-06996-y. [PMID: 37329459 DOI: 10.1007/s10549-023-06996-y] [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: 04/01/2023] [Accepted: 05/25/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND microRNA (miRNAs) are small, non-coding RNAs that mediate post-transcriptional gene silencing. Numerous studies have demonstrated the critical role of miRNAs in the development of breast cancer and ovarian cancer. To reduce potential bias from individual studies, a more comprehensive approach of exploring miRNAs in cancer research is essential. This study aims to explore the role of miRNAs in the development of breast cancer and ovarian cancer. METHODS Abstracts of the publications were tokenized and the biomedical terms (miRNA, gene, disease, species) were identified and extracted for vectorization. Predictive analyses were conducted with four machine learning models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes. Both holdout validation and cross-validation were utilized. Feature importance will be identified for miRNA-cancer networks construction. RESULTS We found that miR-182 is highly specific to female cancers. miR-182 targets different genes in regulating breast cancer and ovarian cancer. Naïve Bayes provided a promising prediction model for breast cancer and ovarian cancer with miRNAs and genes combination, with an accuracy score greater than 60%. Feature importance identified miR-155 and miR-199 are critical for breast cancer and ovarian cancer prediction, with miR-155 being highly related to breast cancer, whereas miR-199 being more associated with ovarian cancer. CONCLUSION Our approach effectively identified potential miRNA biomarkers associated with breast cancer and ovarian cancer, providing a solid foundation for generating novel research hypotheses and guiding future experimental studies.
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Affiliation(s)
- Xin Li
- Ophthalmology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China
| | - Andrea Dai
- Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Richard Tran
- Masters Program in Computer Science, University of Chicago, Chicago, IL, 20833, USA
| | - Jie Wang
- Applied Data Science Program, Syracuse University, Syracuse, NY, 13244, USA.
- MDSight, LLC, Brookeville, MD, 20833, USA.
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Kuang X, Zhong Z, Liang W, Huang S, Luo R, Luo H, Li Y. Bibliometric analysis of 100 top cited articles of heart failure-associated diseases in combination with machine learning. Front Cardiovasc Med 2023; 10:1158509. [PMID: 37304963 PMCID: PMC10248156 DOI: 10.3389/fcvm.2023.1158509] [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: 02/04/2023] [Accepted: 05/03/2023] [Indexed: 06/13/2023] Open
Abstract
Objective The aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure-related machine learning publications. Materials and methods Web of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions. Results The search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method. Conclusions This analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.
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Affiliation(s)
- Xuyuan Kuang
- Department of Hyperbaric Oxygen, Xiangya Hospital, Changsha, China
- National Research Center of Geriatic Diseases (Xiangya Hospital), Changsha, China
| | - Zihao Zhong
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Wei Liang
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Suzhen Huang
- The Big Data Institute, Central South University, Changsha, China
| | - Renji Luo
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
| | - Hui Luo
- National Research Center of Geriatic Diseases (Xiangya Hospital), Changsha, China
- Department of Anesthesiology, Xiangya Hospital, Changsha, China
| | - Yongheng Li
- Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China
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11
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Anand V, Hu H, Weston AD, Scott CG, Michelena HI, Pislaru SV, Carter RE, Pellikka PA. Machine learning-based risk stratification for mortality in patients with severe aortic regurgitation. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:188-195. [PMID: 37265866 PMCID: PMC10232267 DOI: 10.1093/ehjdh/ztad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 12/06/2022] [Indexed: 06/03/2023]
Abstract
Aims The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines. Methods and results The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.
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Affiliation(s)
- Vidhu Anand
- Corresponding author. Tel: +507 284 4441, Fax: +507 266 0228,
| | - Hanwen Hu
- Department of Quantitative Health Sciences Research, Mayo Clinic, Jacksonville, FL 32202, USA
| | - Alexander D Weston
- Department of Quantitative Health Sciences Research, Mayo Clinic, Jacksonville, FL 32202, USA
| | - Christopher G Scott
- Department of Quantitative Health Science, Mayo Clinic, Rochester, MN 55905, USA
| | - Hector I Michelena
- Department of Cardiovascular Medicine, Mayo Clinic Rochester Minnesota, 200 First Street SW, Rochester, MN 55905, USA
| | - Sorin V Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic Rochester Minnesota, 200 First Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences Research, Mayo Clinic, Jacksonville, FL 32202, USA
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Guida F, Lenatti M, Keshavjee K, Khatami A, Guergachi A, Paglialonga A. Characterization of Inclination Analysis for Predicting Onset of Heart Failure from Primary Care Electronic Medical Records. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094228. [PMID: 37177432 PMCID: PMC10181219 DOI: 10.3390/s23094228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/05/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
The aim of this study is to characterize the performance of an inclination analysis for predicting the onset of heart failure (HF) from routinely collected clinical biomarkers extracted from primary care electronic medical records. A balanced dataset of 698 patients (with/without HF), including a minimum of five longitudinal measures of nine biomarkers (body mass index, diastolic and systolic blood pressure, fasting glucose, glycated hemoglobin, low-density and high-density lipoproteins, total cholesterol, and triglycerides) is used. The proposed algorithm achieves an accuracy of 0.89 (sensitivity of 0.89, specificity of 0.90) to predict the inclination of biomarkers (i.e., their trend towards a 'survival' or 'collapse' as defined by an inclination analysis) on a labeled, balanced dataset of 40 patients. Decision trees trained on the predicted inclination of biomarkers have significantly higher recall (0.69 vs. 0.53) and significantly higher negative predictive value (0.60 vs. 0.55) than those trained on the average values computed from the measures of biomarkers available before the onset of the disease, suggesting that an inclination analysis can help identify the onset of HF in the primary care patient population from routinely available clinical data. This exploratory study provides the basis for further investigations of inclination analyses to identify at-risk patients and generate preventive measures (i.e., personalized recommendations to reverse the trend of biomarkers towards collapse).
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Affiliation(s)
- Federica Guida
- Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, Italy
| | - Marta Lenatti
- Cnr-Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), 20133 Milan, Italy
| | - Karim Keshavjee
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON M5T 3M6, Canada
| | - Alireza Khatami
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON M5T 3M6, Canada
| | - Aziz Guergachi
- Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5G 2C3, Canada
- Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON M5G 2C3, Canada
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
| | - Alessia Paglialonga
- Cnr-Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), 20133 Milan, Italy
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13
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Ru B, Tan X, Liu Y, Kannapur K, Ramanan D, Kessler G, Lautsch D, Fonarow G. Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study. JMIR Form Res 2023; 7:e41775. [PMID: 37067873 PMCID: PMC10152335 DOI: 10.2196/41775] [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: 09/19/2022] [Revised: 02/16/2023] [Accepted: 02/19/2023] [Indexed: 04/18/2023] Open
Abstract
BACKGROUND Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization. OBJECTIVE Machine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs). METHODS We used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared. RESULTS A total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions. CONCLUSIONS We predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.
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Affiliation(s)
- Boshu Ru
- Merck & Co, Inc, Rahway, NJ, United States
| | - Xi Tan
- Merck & Co, Inc, Rahway, NJ, United States
| | - Yu Liu
- Merck & Co, Inc, Rahway, NJ, United States
| | | | | | - Garin Kessler
- Amazon Web Services Inc, Seattle, WA, United States
- School of Continuing Studies, Georgetown University, Washington, DC, United States
| | | | - Gregg Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, University of California, Los Angeles, Los Angeles, CA, United States
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Gelman R, Hurvitz N, Nesserat R, Kolben Y, Nachman D, Jamil K, Agus S, Asleh R, Amir O, Berg M, Ilan Y. A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial. Biomed Pharmacother 2023; 161:114334. [PMID: 36905809 DOI: 10.1016/j.biopha.2023.114334] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 03/11/2023] Open
Abstract
INTRODUCTION Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens. METHODS Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function. RESULTS The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations. SUMMARY The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.
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Affiliation(s)
- Ram Gelman
- Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Noa Hurvitz
- Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Rima Nesserat
- Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Yotam Kolben
- Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Dean Nachman
- Departments of Cardiology, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Khurram Jamil
- Oberon Sciences and Area 9 Innovation, Stanford University, Palo Alto, CA, USA
| | - Samuel Agus
- Oberon Sciences and Area 9 Innovation, Stanford University, Palo Alto, CA, USA
| | - Rabea Asleh
- Departments of Cardiology, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Offer Amir
- Departments of Cardiology, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel
| | - Marc Berg
- Oberon Sciences and Area 9 Innovation, Stanford University, Palo Alto, CA, USA
| | - Yaron Ilan
- Departments of Medicine, Hadassah Medical Center, and Faculty of Medicine, Hebrew University, Jerusalem, Israel.
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15
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Wu S, Lei L, Hu Y, Jiang L, Fu C, Zhang Y, Zhu L, Huang J, Chen J, Zeng Q. Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation. FUNDAMENTAL RESEARCH 2023. [DOI: 10.1016/j.fmre.2023.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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16
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Barton M, Hamza M, Guevel B. Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation. Cureus 2023; 15:e35037. [PMID: 36942183 PMCID: PMC10023594 DOI: 10.7759/cureus.35037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2023] [Indexed: 02/17/2023] Open
Abstract
Background and objective While the potential of machine learning (ML) in healthcare to positively impact human health continues to grow, the potential for inequity in these methods must be assessed. In this study, we aimed to evaluate the presence of racial bias when five of the most common ML algorithms are used to create models with minimal processing to reduce racial bias. Methods By utilizing a CDC public database, we constructed models for the prediction of healthcare access (binary variable). Using area under the curve (AUC) as our performance metric, we calculated race-specific performance comparisons for each ML algorithm. We bootstrapped our entire analysis 20 times to produce confidence intervals for our AUC performance metrics. Results With the exception of only a few cases, we found that the performance for the White group was, in general, significantly higher than that of the other racial groups across all ML algorithms. Additionally, we found that the most accurate algorithm in our modeling was Extreme Gradient Boosting (XGBoost) followed by random forest, naive Bayes, support vector machine (SVM), and k-nearest neighbors (KNN). Conclusion Our study illustrates the predictive perils of incorporating minimal racial bias mitigation in ML models, resulting in predictive disparities by race. This is particularly concerning in the setting of evidence for limited bias mitigation in healthcare-related ML. There needs to be more conversation, research, and guidelines surrounding methods for racial bias assessment and mitigation in healthcare-related ML models, both those currently used and those in development.
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Affiliation(s)
| | - Mahmoud Hamza
- Quantitative Methods, Harvard School of Public Health, Boston, USA
| | - Borna Guevel
- Quantitative Methods, Harvard School of Public Health, Boston, USA
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17
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Kamio T, Ikegami M, Machida Y, Uemura T, Chino N, Iwagami M. Machine learning-based prognostic modeling of patients with acute heart failure receiving furosemide in intensive care units. Digit Health 2023; 9:20552076231194933. [PMID: 37576718 PMCID: PMC10422900 DOI: 10.1177/20552076231194933] [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] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose This study developed machine learning models to predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation in patients with acute heart failure receiving furosemide in intensive care units. Method An extensive database comprising static and dynamic features obtained from a Japanese hospital chain was used to construct and train the machine learning models. Results The results revealed that the proposed machine learning models predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation with good accuracy. However, the optimal models vary depending on the predicted outcomes. The linear support vector machine classification models exhibited the highest in-hospital mortality and mechanical ventilation prediction accuracy, with the area under the receiver operating characteristic curve of 0.73 and 0.73, respectively, whereas the multi-layer neural network exhibited the highest accuracy for acute renal replacement therapy initiation prediction with an area under the receiver operating characteristic curve of 0.70. Conclusions In conclusion, this study demonstrated that machine learning models could help predict the clinical outcomes of patients with acute heart failure receiving furosemide. However, the optimal models may differ depending on the outcome of interest.
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Affiliation(s)
- Tadashi Kamio
- Division of Critical Care, Shonan Kamakura General Hospital, Kanagawa, Japan
| | - Masaru Ikegami
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Yoshihito Machida
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Tomoko Uemura
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Naotaka Chino
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Masao Iwagami
- Department of Health Services Research, University of Tsukuba, Ibaraki, Japan
- Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
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Sbrollini A, Barocci M, Mancinelli M, Paris M, Raffaelli S, Marcantoni I, Morettini M, Swenne CA, Burattini L. Automatic diagnosis of newly emerged heart failure from serial electrocardiography by repeated structuring & learning procedure. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Kumar M, Noronha S, Rangaraj N, Moiyadi A, Shetty P, Singh VK. Choice of intraoperative ultrasound adjuncts for brain tumor surgery. BMC Med Inform Decis Mak 2022; 22:307. [DOI: 10.1186/s12911-022-02046-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022] Open
Abstract
Abstract
Background
Gliomas are among the most typical brain tumors tackled by neurosurgeons. During navigation for surgery of glioma brain tumors, preoperatively acquired static images may not be accurate due to shifts. Surgeons use intraoperative imaging technologies (2-Dimensional and navigated 3-Dimensional ultrasound) to assess and guide resections. This paper aims to precisely capture the importance of preoperative parameters to decide which type of ultrasound to be used for a particular surgery.
Methods
This paper proposes two bagging algorithms considering base classifier logistic regression and random forest. These algorithms are trained on different subsets of the original data set. The goodness of fit of Logistic regression-based bagging algorithms is established using hypothesis testing. Furthermore, the performance measures for random-forest-based bagging algorithms used are AUC under ROC and AUC under the precision-recall curve. We also present a composite model without compromising the explainability of the models.
Results
These models were trained on the data of 350 patients who have undergone brain surgery from 2015 to 2020. The hypothesis test shows that a single parameter is sufficient instead of all three dimensions related to the tumor ($$p < 0.05$$
p
<
0.05
). We observed that the choice of intraoperative ultrasound depends on the surgeon making a choice, and years of experience of the surgeon could be a surrogate for this dependence.
Conclusion
This study suggests that neurosurgeons may not need to focus on a large set of preoperative parameters in order to decide on ultrasound. Moreover, it personalizes the use of a particular ultrasound option in surgery. This approach could potentially lead to better resource management and help healthcare institutions improve their decisions to make the surgery more effective.
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Le TD, Noumeir R, Rambaud J, Sans G, Jouvet P. Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:142-149. [PMID: 36712317 PMCID: PMC9870264 DOI: 10.1109/ojemb.2022.3209900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/27/2022] [Accepted: 09/22/2022] [Indexed: 02/01/2023] Open
Abstract
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.
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Affiliation(s)
- Thanh-Dung Le
- Biomedical Information Processing Lab, École de Technologie SupérieureUniversity of Québec Montreal QB H3G 1M8 Canada
- Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada
| | - Rita Noumeir
- Biomedical Information Processing Lab, École de Technologie SupérieureUniversity of Québec Montreal QB H3G 1M8 Canada
| | - Jerome Rambaud
- Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada
| | - Guillaume Sans
- Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada
| | - Philippe Jouvet
- Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada
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Widanagamaachchi W, Peterson K, Chapman A, Classen D, Jones M. A flexible framework for visualizing and exploring patient misdiagnosis over time. J Biomed Inform 2022; 134:104178. [PMID: 36064112 DOI: 10.1016/j.jbi.2022.104178] [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: 05/20/2022] [Revised: 07/24/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022]
Abstract
Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.
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Affiliation(s)
- Wathsala Widanagamaachchi
- University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA.
| | - Kelly Peterson
- University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA; Veterans Health Administration Office of Analytics and Performance Integration, 810 Vermont Ave., NW, Washington, 20420, DC, USA.
| | - Alec Chapman
- University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA; Department of Population Health Sciences, University of Utah School of Medicine, Williams Building, Room 1N410, 295 Chipeta Way, Salt Lake City, 84108, UT, USA.
| | - David Classen
- University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA.
| | - Makoto Jones
- University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA.
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22
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Qian L, Zhou Y, Zeng W, Chen X, Ding Z, Shen Y, Qian Y, Tosi D, Silva M, Han Y, Fu X. A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules. Transl Lung Cancer Res 2022; 11:1132-1144. [PMID: 35832446 PMCID: PMC9271446 DOI: 10.21037/tlcr-22-395] [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: 03/28/2022] [Accepted: 06/16/2022] [Indexed: 11/06/2022]
Abstract
Background Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection. Methods Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them. Results The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort. Conclusions Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.
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Affiliation(s)
- Liqiang Qian
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yinjie Zhou
- Department of Thoracic Surgery, Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital), Ningbo, China
| | - Wanqin Zeng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoke Chen
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Zhengping Ding
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yujia Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yifeng Qian
- National Clinical Research Center for Oral Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Davide Tosi
- Thoracic Surgery and Lung Transplant Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Yuchen Han
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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Zhou X, Nakamura K, Sahara N, Asami M, Toyoda Y, Enomoto Y, Hara H, Noro M, Sugi K, Moroi M, Nakamura M, Huang M, Zhu X. Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning. Life (Basel) 2022; 12:life12060776. [PMID: 35743806 PMCID: PMC9224610 DOI: 10.3390/life12060776] [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: 05/07/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 12/05/2022] Open
Abstract
Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan−Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29−3.37, p = 0.003), and 0.26 (95%CI 0.11−0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
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Affiliation(s)
- Xue Zhou
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu 965-8580, Japan;
| | - Keijiro Nakamura
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
- Correspondence: (K.N.); (X.Z.); Tel.: +81-3-468-1251 (K.N.); +81-242-37-2771 (X.Z.)
| | - Naohiko Sahara
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Masako Asami
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Yasutake Toyoda
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Yoshinari Enomoto
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Hidehiko Hara
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Mahito Noro
- Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital, Odawara 250-0873, Japan; (M.N.); (K.S.)
| | - Kaoru Sugi
- Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital, Odawara 250-0873, Japan; (M.N.); (K.S.)
| | - Masao Moroi
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Masato Nakamura
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan; (N.S.); (M.A.); (Y.T.); (Y.E.); (H.H.); (M.M.); (M.N.)
| | - Ming Huang
- Division of Information Science, Nara Institute of Science and Technology, Ikoma 630-0192, Japan;
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu 965-8580, Japan;
- Correspondence: (K.N.); (X.Z.); Tel.: +81-3-468-1251 (K.N.); +81-242-37-2771 (X.Z.)
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Matsuo S, Ushida T, Emoto R, Moriyama Y, Iitani Y, Nakamura N, Imai K, Nakano-Kobayashi T, Yoshida S, Yamashita M, Matsui S, Kajiyama H, Kotani T. Machine learning prediction models for postpartum depression: A multicenter study in Japan. J Obstet Gynaecol Res 2022; 48:1775-1785. [PMID: 35438215 DOI: 10.1111/jog.15266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/14/2022] [Accepted: 04/04/2022] [Indexed: 11/28/2022]
Abstract
AIM Postpartum depression (PPD) and perinatal mental health care are of growing importance worldwide. Here we aimed to develop and validate machine learning models for the prediction of PPD, and to evaluate the usefulness of the recently adopted 2-week postpartum checkup in some parts of Japan for the identification of women at high risk of PPD. METHODS A multicenter retrospective study was conducted using the clinical data of 10 013 women who delivered at ≥35 weeks of gestation at 12 maternity care hospitals in Japan. PPD was defined as an Edinburgh Postnatal Depression Scale score of ≥9 points at 4 weeks postpartum. We developed prediction models using conventional logistic regression and four machine learning algorithms based on the information that can be routinely collected in daily clinical practice. The model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS In the machine learning models developed using clinical data before discharge, the AUROCs were similar to those in the conventional logistic regression models (AUROC, 0.569-0.630 vs. 0.626). The incorporation of additional 2-week postpartum checkup data into the model significantly improved the predictive performance for PPD compared to that without in the Ridge regression and Elastic net (AUROC, 0.702 vs. 0.630 [p < 0.01] and 0.701 vs. 0.628 [p < 0.01], respectively). CONCLUSIONS Our machine learning models did not achieve better predictive performance for PPD than conventional logistic regression models. However, we demonstrated the usefulness of the 2-week postpartum checkup for the identification of women at high risk of PPD.
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Affiliation(s)
- Seiko Matsuo
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takafumi Ushida
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Ryo Emoto
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshinori Moriyama
- Department of Obstetrics and Gynecology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Yukako Iitani
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Noriyuki Nakamura
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kenji Imai
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomoko Nakano-Kobayashi
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | | | | | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroaki Kajiyama
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomomi Kotani
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
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Zhang R, Wang J. Machine Learning-Based Prediction of Subsequent Vascular Events After 6 Months in Chinese Patients with Minor Ischemic Stroke. Int J Gen Med 2022; 15:3797-3808. [PMID: 35418774 PMCID: PMC9000551 DOI: 10.2147/ijgm.s356373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Background To develop and validate a machine learning model for predicting subsequent vascular events (SVE) 6 months after mild ischemic stroke (MIS) in Chinese patients. Methods A retrospective analysis was performed on 495 newly diagnosed MIS patients by collecting their basic information, past medical history, initial NIHSS score, symptoms, obstruction sites of MIS, and MRI results. According to the ratio of 7:3, the dataset was divided into a training set (n=346) and a testing set (n=149) through stratified random sampling. In the training set, the recursive feature elimination (RFE) was used to select the optimal combination of features, and two machine learning algorithms, including the logistic regression (LR) and support vector machines (SVM), were used to build the prediction model, which was further validated by using 5-fold cross-validation. The receiver operating characteristic (ROC) curve was used on the testing set to evaluate the model’s performance, and the area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. The calibration curve and decision curve of the two models were further compared. Results SVE occurred in 56 cases (11.3%) of 495 patients with MIS during the 6-month follow-up. Finally, the best 15 predictive features were selected, and the top three predictive features were diabetes, posterior cerebral artery lesion, and fasting blood glucose in order. In the testing set, the AUC of the LR model was 0.929 (95% CI: 0.875–0.964), and its accuracy, sensitivity, and specificity were 0.832, 0.765, and 0.841, respectively. The AUC of the SVM model was 0.992 (95% CI: 0.962–1.000), and its accuracy, sensitivity, and specificity were 0.966, 0.824, and 0.985, respectively. The SVM model’s discrimination, calibration, and clinical validity are better than those of the LR model. Conclusion The predictive models developed using machine learning methods can predict the risk of SVE after 6 months following MIS in Chinese patients.
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Affiliation(s)
- Rong Zhang
- Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Suzhou, 215300, People’s Republic of China
| | - Jingfeng Wang
- Department of Neurology, The Second People’s Hospital of Kunshan, Suzhou, 215300, People’s Republic of China
- Correspondence: Jingfeng Wang, Department of Neurology, The Second People’s Hospital of Kunshan, Suzhou, 215300, People’s Republic of China, Tel +86-15962508528, Fax +86-512-57557843, Email
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Mulhern R, Roostaei J, Schwetschenau S, Pruthi T, Campbell C, MacDonald Gibson J. A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems. ENVIRONMENTAL RESEARCH 2022; 204:112146. [PMID: 34597659 DOI: 10.1016/j.envres.2021.112146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/24/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Lead in drinking water continues to put children at risk of irreversible neurological impairment. Understanding drinking water system characteristics that influence blood lead levels is needed to prevent ongoing exposures. This study sought to assess the relationship between children's blood lead levels and drinking water system characteristics using machine-learned Bayesian networks. Blood lead records from 2003 to 2017 for 40,742 children in Wake County, North Carolina were matched with the characteristics of 178 community water systems and sociodemographic characteristics of each child's neighborhood. Bayesian networks were machine-learned to evaluate the drinking water variables associated with blood lead levels ≥2 μg/dL and ≥5 μg/dL. The model was used to predict geographic areas and water utilities with increased lead exposure risk. Drinking water characteristics were not significantly associated with children's blood lead levels ≥5 μg/dL but were important predictors of blood lead levels ≥2 μg/dL. Whether 10% of water samples exceeded 2 ppb of lead in the most recent year prior to the blood test was the most important water system predictor and increased the risk of blood lead levels ≥2 μg/dL by 42%. The model achieved an area under the receiver operating characteristic curve of 0.792 (±0.8%) during ten-fold cross validation, indicating good predictive performance. Water system characteristics may thus be used to predict areas that are at risk of higher blood lead levels. Current drinking water regulatory thresholds for lead may be insufficient to detect the levels in drinking water associated with children's blood lead levels.
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Affiliation(s)
- Riley Mulhern
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
| | - Javad Roostaei
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Sara Schwetschenau
- Department of Civil and Environmental Engineering, College of Engineering, Wayne State University, 5050 Anthony Wayne Dr., Detroit, Michigan, 48202, USA
| | - Tejas Pruthi
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Chris Campbell
- Environmental Working Group, 1436 U St. NW, Suite 100, Washington, DC, 20009, USA
| | - Jacqueline MacDonald Gibson
- Department of Environmental and Occupational Health, School of Public Health, Indiana University, 1025 East 7(th)Street, Bloomington, IN, 47405, USA
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Shen J, Zhang D, liang B. Prediction of host age and sex classification through gut microbes based on machine learning. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2021.108280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021; 159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.
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Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Barbara R Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom; School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia.
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Greer ML, Davis K, Stack BC. Machine learning can identify patients at risk of hyperparathyroidism without known calcium and intact parathyroid hormone. Head Neck 2021; 44:817-822. [PMID: 34953008 DOI: 10.1002/hed.26970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/01/2021] [Accepted: 12/16/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND To prove the concept of diagnosing primary hyperparathyroidism (pHPT) without calcium and parathyroid hormone (PTH) values and identifying potential risk factors for pHPT. METHODS Data were extracted from the clinical data warehouse (CDW) at the University of Arkansas for Medical Sciences (UAMS) Epic EHR (2014-2019). RESULTS 1737 patients with over 185 000 rows of clinical data were provided in a relational structure and processed/flattened to facilitate modeling. Phenotype elements were identified for pHPT without advance knowledge of calcium and PTH levels. The area under the curve (AUC) for the prediction of pHPT using our model was 0.86 with sensitivity and specificity of 0.8953 and 0.6686, respectively, using a 0.45 probability threshold. CONCLUSION Primary hyperparathyroidism was predicted from a dataset excluding calcium and PTH data with 86% accuracy. This approach needs to be validated/refined on larger samples of data and plans are in place to do this with other regional/national datasets.
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Affiliation(s)
- Melody L Greer
- Department of Health Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Kyle Davis
- Department of Otolaryngology - Head and Neck Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Brendan C Stack
- Department of Otolaryngology - Head and Neck Surgery, Southern Illinois University School of Medicine, Springfield, Illinois, USA
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Kishi T. New Era of Machine Learning Prediction of the Development of Cardiac Events in Heart Failure and Preserved Ejection Fraction. Circ J 2021; 86:47-48. [PMID: 34526444 DOI: 10.1253/circj.cj-21-0694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Takuya Kishi
- Department of Graduate School of Medicine (Cardiology), International University of Health and Welfare
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Wang Q, Li B, Chen K, Yu F, Su H, Hu K, Liu Z, Wu G, Yan J, Su G. Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure. ESC Heart Fail 2021; 8:5363-5371. [PMID: 34585531 PMCID: PMC8712774 DOI: 10.1002/ehf2.13627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/25/2021] [Accepted: 09/08/2021] [Indexed: 11/09/2022] Open
Abstract
AIMS Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study explored the feasibility of ML methods for predicting the risk of MA in hospitalized HF patients. METHODS AND RESULTS We evaluated the baseline data and MA events of 2794 hospitalized HF patients in the HF cohort in Anhui Province and randomly divided the study population into training and validation sets in a 7:3 ratio. The Lasso-logistic regression, multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost) algorithms were used to construct risk prediction models in the training set, and model performance was verified in the validation set. The area under the receiver operating characteristic curve (AUC) and Brier score were employed to evaluate the discrimination and calibration of the model, respectively. Clinical utility of the Lasso-logistic regression model was analysed using decision curve analysis (DCA). The median (Q1, Q3) age of the study population was 70 (61, 77) years, and 39.5% were female. MA events occurred in 117 patients (4.2%) during hospitalization. In the training set (n = 1964), the AUC of the XGBoost model was 0.998 [95% confidence interval (CI) 0.997-1.000], which was higher than the other models (all P < 0.001). In the validation set (n = 830), there was no significant difference in AUC of Lasso-logistic model 1 [AUC: 0.867 (95% CI 0.819-0.915)], Lasso-logistic model 2 [AUC: 0.828 (95% CI 0.764-0.892)], MARS model [AUC: 0.852 (95% CI 0.793-0.910)], RF model [AUC: 0.804 (95% CI 0.726-0.881)], and XGBoost model [AUC: 0.864 (95% CI 0.810-0.918); all P > 0.05], which were higher than that of CART model [AUC: 0.743 (95% CI 0.661-0.824); all P < 0.05]. Brier scores for all prediction models were less than 0.05. DCA results showed that the Lasso-logistic model had a net clinical benefit. Oral antiarrhythmic drug, left bundle branch block, serum magnesium, d-dimer, and random blood glucose were significant predictors in half or more of the models. CONCLUSIONS The current study findings suggest that ML models based on the Lasso-logistic regression, MARS, RF, and XGBoost algorithms can effectively predict the risk of MA in hospitalized HF patients. The Lasso-logistic model had better clinical interpretability and ease of use than the other models.
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Affiliation(s)
- Qi Wang
- Division of CardiologyJinan Central Hospital, Cheeloo College of Medicine, Shandong UniversityNo. 105 Jiefang RoadJinan250013China
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Bin Li
- Division of CardiologyJinan Central Hospital, Cheeloo College of Medicine, Shandong UniversityNo. 105 Jiefang RoadJinan250013China
| | - Kangyu Chen
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Fei Yu
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Hao Su
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Kai Hu
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Zhiquan Liu
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Guohong Wu
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Ji Yan
- Heart Failure Center, The First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefeiChina
| | - Guohai Su
- Division of CardiologyJinan Central Hospital, Cheeloo College of Medicine, Shandong UniversityNo. 105 Jiefang RoadJinan250013China
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Papadopoulos TG, Plati D, Tripoliti EE, Goletsis Y, Naka KK, Rammos A, Bechlioulis A, Watson C, McDonald K, Ledwidge M, Pharithi R, Gallagher J, Fotiadis DI. Heart Failure diagnosis based on deep learning techniques. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1757-1760. [PMID: 34891627 DOI: 10.1109/embc46164.2021.9630409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%.
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Cheng L, Qiu Y, Schmidt BJ, Wei GW. Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure. J Pharmacokinet Pharmacodyn 2021; 49:39-50. [PMID: 34637069 PMCID: PMC8837528 DOI: 10.1007/s10928-021-09785-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.
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Affiliation(s)
- Limei Cheng
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA.
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Brian J Schmidt
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.,Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
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de Siqueira VS, Borges MM, Furtado RG, Dourado CN, da Costa RM. Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med 2021; 120:102165. [PMID: 34629153 DOI: 10.1016/j.artmed.2021.102165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/07/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
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Affiliation(s)
- Vilson Soares de Siqueira
- Federal Institute of Tocantins, Av. Bernado Sayão, S/N, Santa Maria, Colinas do Tocantins, TO, Brazil; Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
| | - Moisés Marcos Borges
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Rogério Gomes Furtado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil
| | - Colandy Nunes Dourado
- Diagnostic Imaging Center - CDI, Av. Portugal, 1155, St. Marista, Goiânia, GO, Brazil. http://www.cdigoias.com.br
| | - Ronaldo Martins da Costa
- Federal University of Goias, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, GO, Brazil.
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Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure. Cells 2021; 10:cells10092430. [PMID: 34572079 PMCID: PMC8470162 DOI: 10.3390/cells10092430] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/23/2022] Open
Abstract
Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual’s quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.
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Wang Z, Chen X, Tan X, Yang L, Kannapur K, Vincent JL, Kessler GN, Ru B, Yang M. Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction. JOURNAL OF HEALTH ECONOMICS AND OUTCOMES RESEARCH 2021; 8:6-13. [PMID: 34414250 PMCID: PMC8322198 DOI: 10.36469/jheor.2021.25753] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL models to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from the IBM® MarketScan® Commercial and Medicare Supplement databases between January 1, 2015 and December 31, 2017. A sequential model architecture based on bi-directional long short-term memory (Bi-LSTM) layers was utilized. For DL models to predict HF hospitalizations and worsening HF events, we utilized two study designs: with and without a buffer window. For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. Results: A total of 47 498 HFrEF patients were included; 9427 with at least one HF hospitalization. The best AUCs of DL models without a buffer window in predicting HF hospitalizations and worsening HF events in the total patient cohort were 0.977 and 0.972; with a 7-day buffer window the best AUCs were 0.573 and 0.608, respectively. The best AUCs in predicting 30- and 90-day readmissions in all adult patients were 0.597 and 0.614, respectively. An AUC of 0.861 was attained for prediction of 90-day readmission in patients aged 18-64. For all outcomes assessed, the DL approach outperformed traditional machine learning models. Discussion: The DL approach can automate feature engineering during the model learning, which can increase the clinical applicability and lead to comparable or better model performance. However, the lack of granular clinical data, and sample size and imbalance issues may have limited the model's performance. Conclusions: A DL approach using Bi-LSTM was shown to be a feasible and useful tool to predict HF-related outcomes. This study can help inform the future development and deployment of predictive tools to identify high-risk HFrEF patients and ultimately facilitate targeted interventions in clinical practice.
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Affiliation(s)
- Zhibo Wang
- Merck & Co., Inc., Kenilworth, NJ, USA; College of Engineering and Computer Science, University of Central Florida, Orlando, FL, USA
| | - Xin Chen
- Merck & Co., Inc., Kenilworth, NJ, USA
| | - Xi Tan
- Merck & Co., Inc., Kenilworth, NJ, USA
| | | | | | | | - Garin N Kessler
- Amazon Web Services Inc., Seattle, WA, USA; Georgetown University, Seattle, WA, USA
| | - Boshu Ru
- Merck & Co., Inc., Kenilworth, NJ, USA
| | - Mei Yang
- Merck & Co., Inc., Kenilworth, NJ, USA
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Abstract
PURPOSE OF REVIEW Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician with the basics of ML, as well as this technologies future utility in HF diagnosis and the potential impact on patient outcomes. RECENT FINDINGS Recent studies applying ML methods to unique data sets available from electrocardiography, vectorcardiography, echocardiography, and electronic health records show significant promise for improving diagnosis, enhancing detection, and advancing treatment of HF. Innovations in both supervised and unsupervised methods have heightened the diagnostic accuracy of models developed to identify the presence of HF and further augmentation of model capabilities are likely utilizing ensembles of ML algorithms derived from different techniques. SUMMARY This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management.
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Affiliation(s)
- William E Sanders
- University of North Carolina at Chapel Hill, Chapel Hill
- CorVista Health, Inc., Cary, North Carolina, USA
| | - Tim Burton
- CorVista Health, Toronto, Ontario, Canada
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Kanwar MK, Kilic A, Mehra MR. Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians. J Heart Lung Transplant 2021; 40:414-425. [PMID: 33775520 DOI: 10.1016/j.healun.2021.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/26/2021] [Accepted: 02/22/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and make accurate predictions. The application of AI combined with "big data" from the electronic health records, is poised to impact how we take care of patients. In recent years, an expanding body of literature has been published using ML in cardiovascular health care, including mechanical circulatory support (MCS). This primer article provides an overview for clinicians on relevant concepts of ML and AI, reviews predictive modeling concepts in ML and provides contextual reference to how AI is being adapted in the field of MCS. Lastly, it explains how these methods could be incorporated in the practices of medicine to improve patient outcomes.
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Affiliation(s)
- Manreet K Kanwar
- Cardiovascular Institute at Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Arman Kilic
- Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Mandeep R Mehra
- Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School, Boston, Massachusetts.
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Senbekov M, Saliev T, Bukeyeva Z, Almabayeva A, Zhanaliyeva M, Aitenova N, Toishibekov Y, Fakhradiyev I. The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. Int J Telemed Appl 2020; 2020:8830200. [PMID: 33343657 PMCID: PMC7732404 DOI: 10.1155/2020/8830200] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. PURPOSE The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.
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Affiliation(s)
- Maksut Senbekov
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Timur Saliev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | | | - Nazym Aitenova
- NJSC “Astana Medical University”, Nur-Sultan, Kazakhstan
| | | | - Ildar Fakhradiyev
- S.D. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
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