{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:09:30Z","timestamp":1785337770691,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Acute respiratory distress syndrome (ARDS) represents a critical complication in polytrauma patients, characterized by diffuse lung inflammation and bilateral pulmonary infiltrates with mortality rates reaching 45% in intensive care units (ICU). The heterogeneous nature of ARDS and complex clinical presentation in severely injured patients poses substantial diagnostic challenges, necessitating early prediction tools to guide timely interventions. Machine learning (ML) algorithms have emerged as promising approaches for clinical decision support, demonstrating superior performance compared to traditional scoring systems in capturing complex patterns within high-dimensional medical data. Based on the identified research gaps in early ARDS prediction for polytrauma populations, our study aimed to: (i) develop a balanced random forest (BRF) ML model for early ARDS prediction in critically ill polytrauma patients, (ii) identify the most predictive clinical features using ANOVA-based feature selection, and (iii) evaluate model performance using comprehensive metrics addressing class imbalance challenges. Methods: This retrospective cohort study analyzed 407 polytrauma patients admitted to the ICU of the Center of Traumatology and Major Burns of Ben Arous, Tunisia, between 2017 and 2021. We implemented a comprehensive ML pipeline that incorporates Tomek Links undersampling, ANOVA F-test feature selection for the top 10 predictive variables, and SMOTE oversampling with a conservative sampling rate of 0.3. The BRF classifier was trained with class weighting and evaluated using stratified 5-fold cross-validation. Performance metrics included AUROC, PR-AUC, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Results: Among 407 patients, 43 developed ARDS according to the Berlin definition, representing a 10.57% incidence. The BRF model demonstrated exceptional predictive performance with an AUROC of 0.98, a sensitivity of 0.91, a specificity of 0.80, an F1-score of 0.84, and an MCC of 0.70. Precision\u2013recall AUC reached 0.86, demonstrating robust performance despite class imbalance. During stratified cross-validation, AUROC values ranged from 0.93 to 0.99 across folds, indicating consistent model stability. The top 10 selected features included procalcitonin, PaO2 at ICU admission, 24-h pH, massive transfusion, total fluid resuscitation, presence of pneumothorax, alveolar hemorrhage, pulmonary contusion, hemothorax, and flail chest injury. Conclusions: Our BRF model provides a robust, clinically applicable tool for early prediction of ARDS in polytrauma patients using readily available clinical parameters. The comprehensive two-step resampling approach, combined with ANOVA-based feature selection, successfully addressed class imbalance while maintaining high predictive accuracy. These findings support integrating ML approaches into critical care decision-making to improve patient outcomes and resource allocation. External validation in diverse populations remains essential for confirming generalizability and clinical implementation.<\/jats:p>","DOI":"10.3390\/jcm14248934","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T10:06:56Z","timestamp":1766052416000},"page":"8934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Early Prediction of Acute Respiratory Distress Syndrome in Critically Ill Polytrauma Patients Using Balanced Random Forest ML: A Retrospective Cohort Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5009-9959","authenticated-orcid":false,"given":"Nesrine Ben El Hadj","family":"Hassine","sequence":"first","affiliation":[{"name":"Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, Tunis El-Manar University, Tunis 1006, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3027-1680","authenticated-orcid":false,"given":"Sabri","family":"Barbaria","sequence":"additional","affiliation":[{"name":"Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, Tunis El-Manar University, Tunis 1006, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1531-8144","authenticated-orcid":false,"given":"Omayma","family":"Najah","sequence":"additional","affiliation":[{"name":"Anesthesia and Intensive Care Department, Pierre-Wertheimer Neurological Hospital, 69000 Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2214-4667","authenticated-orcid":false,"given":"Halil \u0130brahim","family":"Ceylan","sequence":"additional","affiliation":[{"name":"Physical Education and Sports Teaching Department, Faculty of Sports Sciences, Ataturk University, 25240 Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8446-7123","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[{"name":"Centre for Responsible Innovation in Big Data and AI (BRAIN), Business School, Birmingham City University (BCU), Birmingham B15 3TN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8286-5483","authenticated-orcid":false,"given":"Lotfi","family":"Rebai","sequence":"additional","affiliation":[{"name":"Intensive Care Unit Department, Traumatology and Severe Burns Center of Ben Arous, Ben Arous 2013, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4615-6687","authenticated-orcid":false,"given":"Raul Ioan","family":"Muntean","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, Faculty of Law and Social Sciences, University \u201c1 Decembrie 1918\u201d of Alba Iulia, 510009 Alba Iulia, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8091-1856","authenticated-orcid":false,"given":"Ismail","family":"Dergaa","sequence":"additional","affiliation":[{"name":"High Institute of Sport and Physical Education of Ksar Said, University of Manouba, Manouba 2010, Tunisia"},{"name":"Physical Activity Research Unit, Sport and Health (UR18JS01), National Observatory of Sports, Tunis 1003, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanene","family":"Boussi Rahmouni","sequence":"additional","affiliation":[{"name":"Research Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, Tunis El-Manar University, Tunis 1006, Tunisia"},{"name":"The Computer Science Research Centre, West of University of the England (UWE Bristol), Bristol BS16 1QY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"ref_1","first-page":"2526","article-title":"Acute respiratory distress syndrome: The Berlin Definition","volume":"307","author":"Ranieri","year":"2012","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1007\/s00134-012-2682-1","article-title":"The Berlin definition of ARDS: An expanded rationale, justification, and supplementary material","volume":"38","author":"Ferguson","year":"2012","journal-title":"Intensive Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e66615","DOI":"10.2196\/66615","article-title":"Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis","volume":"27","author":"Yang","year":"2025","journal-title":"J. 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