Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates.

Autor: AlShehhi A; Biomedical Engineering Department,College of Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.; Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates., Almansoori TM; Department of Radiology, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates., Alsuwaidi AR; Department of Pediatrics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates., Alblooshi H; Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jan 11; Vol. 19 (1), pp. e0291373. Date of Electronic Publication: 2024 Jan 11 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0291373
Abstrakt: Background: The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.
Methods: We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.
Results: The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.
Conclusion: Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 AlShehhi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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