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This study develops and assesses machine learning models to predict ICU mortality early among septic patients using retrospective computerized ICU records from 280 hospitals (2014–2015). 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Al-Ansari 1, Fatima A. Bahman Nejad 1, Roudha J. Al-Nasr 1, Johayra Prithula 2, Tawsifur Rahman 3, Anwarul Hasan 4, Muhammad E. H. Chowdhury 5, * and Mohammed Fasihul Alam 1, *  \nAcademic Editors: Daniel L. Herr and Frank Klawonn  \nReceived: 15 March 2025  \nRevised: 26 April 2025  \nAccepted: 15 May 2025  \nPublished: 16 May 2025  \nCitation: Al-Ansari, A.A.; Nejad, F.A.B.; Al-Nasr, R.J.; Prithula, J.; Rahman, T.; Hasan, A.; Chowdhury, M.E.H.; Alam, M.F. Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique. J. Clin. Med. 2025, 14, 3495. [https://](https://)[ ](https://)[doi.org/10.3390/jcm14103495](doi.org/10.3390/jcm14103495)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar;  \n[aa1603886@qu.edu.qa](aa1603886@qu.edu.qa) (A.A.A.-A.); [fb1517563@qu.edu.qa](fb1517563@qu.edu.qa) (F.A.B.N.); [ra1602873@qu.edu.qa](ra1602873@qu.edu.qa) (R.J.A.-N.)  \n2 Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh; [prithulajohayra@gmail.com](prithulajohayra@gmail.com)  \n3 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; [tawsifurrahman.1426@gmail.com](tawsifurrahman.1426@gmail.com)  \n4 Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; [ahasan@qu.edu.qa](ahasan@qu.edu.qa)  \n5 Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar  \n* [Correspondence: malam@qu.edu.qa](Correspondence: malam@qu.edu.qa) (M.E.H.C.); [mchowdhury@qu.edu.qa](mchowdhury@qu.edu.qa) (M.F.A.)  \nAbstract: Introduction: Sepsis leads to substantial global health burdens in terms of morbidity and mortality and is associated with numerous risk factors. It is crucial to identify sepsis at an early stage in order to limit its escalation and sequelae associated with the condition. The purpose of this research is to predict ICU mortality early and evaluate the predictive accuracy of machine learning algorithms for ICU mortality among septic patients. Methods: The study used a retrospective cohort from computerized ICU records accumulated from 280 hospitals between 2014 and 2015 . Initially the sample size was 23.47K. Several machine learning models were trained, validated, and tested using five-fold cross-validation, and three sampling strategies (Under-Sampling, OverSampling, and Combination) . Results: The under-sampled approach combined with augmentation for the Extra Trees model produced the best performance with Accuracy, Precision, Sensitivity, Specificity, F1-Score, and AUC of 90.99%, 84.16%, 94.89%, 88.48%, 89.20%, and 91.69%, respectively, with Top 30 features. For Over-Sampling, the Top 29 combined features showed the best performance with Accuracy, Precision, Sensitivity, Specificity, F1-Score, and AUC of 82.99%, 51.38%, 71.72%, 85.41%, 59.87%, and 78.56%, respectively. For Down-Sampling, the Top 31 combined features produced Accuracy, Precision, Sensitivity, Specificity, F1-Score, and AUC of 81.78%, 49.08%, 79.76%, 82.21%, 60.76%, and 80.98%, respectively. Conclusions: Machine learning models can reliably predict ICU mortality when suitable clinical predictors are utilized. The study showed that the proposed Extra Trees model can predict ICU mortality with an accuracy of 90.99% accuracy using only single-entry data. Incorporating longitudinal data could further enhance model performance.  \nKeywords: sepsis; ICU mortality; prediction; machine learning; over-","cbCaitvRYB50Z2x8","https://ap.wps.com/l/cbCaitvRYB50Z2x8","pdf",2079234,21,"English","# Abstract\n# Background\n## Disease burden and urgency of early sepsis prediction\n## ICU mortality trends and associated risk factors\n# Methods\n## Data source and cohort design\n## Model training, validation, and testing\n## Sampling strategies\n# Results\n## Under-sampling with augmentation for Extra Trees\n## Over-sampling and down-sampling feature sets\n# Conclusions","[{\"question\":\"What is the goal of this research?\",\"answer\":\"To predict ICU mortality early among septic patients and to evaluate how accurately different machine learning algorithms perform for this task.\"},{\"question\":\"How was the dataset constructed and what time period was used?\",\"answer\":\"The study uses a retrospective cohort from computerized ICU records collected from 280 hospitals between 2014 and 2015.\"},{\"question\":\"Which approach produced the best prediction performance?\",\"answer\":\"The under-sampled approach combined with augmentation for the Extra Trees model achieved the best overall results across metrics such as accuracy, sensitivity, specificity, F1-score, and AUC.\"}]","Predicting ICU Mortality Among Septic Patients Using Machine Learning Technique | PDF",53]