{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:05:29Z","timestamp":1790787929675,"version":"4.1.0"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005283","name":"FUNCAP","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005283","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Sepsis remains a leading cause of mortality in intensive care units (ICUs) worldwide. Machine learning models for clinical prediction must be accurate, fair, transparent, and reliable to ensure that physicians feel confident in their decision-making processes. Methods: We used the MIMIC-IV (version 3.1) database to evaluate several machine learning architectures, including Logistic Regression, XGBoost, LightGBM, LSTM (Long Short-Term Memory) networks and Transformer models. We predicted three main clinical targets\u2014hospital mortality, length of stay, and septic shock onset\u2014using artificial intelligence algorithms, with respect for responsible AI principles. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results: The XGBoost model demonstrated superior performance in prediction tasks, particularly for hospital mortality (AUROC 0.874), outperforming traditional LSTM networks, Transformers, and linear baselines. The importance analysis of the variables confirmed the clinical relevance of the model. Conclusions: While XGBoost and ensemble algorithms demonstrate superior predictive power for sepsis prognosis, their clinical adoption necessitates robust explainability mechanisms to gain trust among doctors.<\/jats:p>","DOI":"10.3390\/jcm15062251","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:10:43Z","timestamp":1773655843000},"page":"2251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Responsible AI for Sepsis Prediction: Bridging the Gap Between Machine Learning Performance and Clinical Trust"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6500-3729","authenticated-orcid":false,"given":"Thiago Q.","family":"Oliveira","sequence":"first","affiliation":[{"name":"Department of Computer Science, Federal Institute of Cear\u00e1, Fortaleza 60040-531, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9005-3508","authenticated-orcid":false,"given":"Leandro A.","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza 60440-900, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8165-2390","authenticated-orcid":false,"given":"Fl\u00e1vio R. C.","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza 60440-900, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8220-8582","authenticated-orcid":false,"given":"Jo\u00e3o B. F.","family":"Filho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza 60440-900, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5716-7905","authenticated-orcid":false,"given":"Khalil F.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Emergency Department, General Hospital of Fortaleza, Fortaleza 60150-160, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2029-6454","authenticated-orcid":false,"given":"Daniel A. B.","family":"Tavares","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal Institute of Cear\u00e1, Fortaleza 60040-531, CE, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2025, November 30). Fact Sheets, Detail, Sepsis. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/sepsis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jinf.2016.11.006","article-title":"Hospital-related cost of sepsis: A systematic review","volume":"74","author":"Arefian","year":"2017","journal-title":"J. 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