{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:00:10Z","timestamp":1787504410922,"version":"build-2736575974"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Recovery and Resilience Plan","award":["PE00000007"],"award-info":[{"award-number":["PE00000007"]}]},{"name":"One Health Basic and Translational Research Actions addressing Unmet Needs on Emerging Infectious Diseases","award":["B83C22004750006"],"award-info":[{"award-number":["B83C22004750006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Sepsis is a leading cause of morbidity and mortality worldwide, and its outcomes depend on early recognition and timely intervention. Conventional clinical scores and biomarkers provide prognostic value but often lack accuracy for individualized prediction. Machine learning (ML) offers the ability to integrate multidimensional data to improve risk stratification. We analyzed 477 patients admitted to our hospital, including 251 with sepsis, 100 with septic shock, and 126 controls. Demographic, clinical, and laboratory data were collected. Univariate correlation analyses explored associations with sepsis severity and mortality (in-hospital, 30-day, and 90-day). Several ML models were tested, with performance assessed by area under the receiver operating characteristic curve (AUC-ROC) and Matthews\u2019s correlation coefficient (MCC). Model interpretability was evaluated using SHAP (SHapley Additive exPlanations). Sepsis severity and mortality correlated with biomarkers (procalcitonin, mid-regional pro-adrenomedullin, lactate) and clinical scores (SOFA, qSOFA). In-hospital mortality was associated with ADM, catecholamine use, and SOFA, while 90-day mortality involved smoking and Gram-negative or polymicrobial infections. Different machine learning models were evaluated, and the model achieving the highest performance on the validation set was selected. The selected model either outperformed or demonstrated comparable performance to logistic regression, depending on the specific prediction task (AUC 0.99 for sepsis, 0.96 for septic shock, 0.70 for ICU admission; 0.90, 0.72, and 0.87 for in-hospital, 30-day, and 90-day mortality). SHAP confirmed the clinical relevance of these predictors. ML models integrating clinical and biochemical data outperform conventional methods in predicting sepsis progression and mortality, while maintaining interpretability. These findings support the use of ML-based tools for early diagnosis and personalized risk stratification in sepsis, though external validation is required before clinical application.<\/jats:p>","DOI":"10.3390\/ijms27062721","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:35:57Z","timestamp":1773740157000},"page":"2721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning Models for Sepsis: From Early Detection to Short- and Long-Term Prognosis"],"prefix":"10.3390","volume":"27","author":[{"given":"Maria Vittoria","family":"Ristori","sequence":"first","affiliation":[{"name":"National PhD Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy"},{"name":"Research Unit of Clinical Laboratory Science, Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy"},{"name":"Operative Research Unit of Laboratory, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9759-5492","authenticated-orcid":false,"given":"Filippo","family":"Ruffini","sequence":"additional","affiliation":[{"name":"Research Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-4997","authenticated-orcid":false,"given":"Silvia","family":"Spoto","sequence":"additional","affiliation":[{"name":"Diagnostic and Therapeutic Medicine Department, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Cammarata","sequence":"additional","affiliation":[{"name":"Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"La Vaccara","sequence":"additional","affiliation":[{"name":"Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6817-4492","authenticated-orcid":false,"given":"Lucrezia","family":"Bani","sequence":"additional","affiliation":[{"name":"Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7058-1945","authenticated-orcid":false,"given":"Damiano","family":"Caputo","sequence":"additional","affiliation":[{"name":"Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-072X","authenticated-orcid":false,"given":"Paolo","family":"Soda","sequence":"additional","affiliation":[{"name":"Research Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy"},{"name":"Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Ume\u00e5 University, 901 87 Ume\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-7447","authenticated-orcid":false,"given":"Valerio","family":"Guarrasi","sequence":"additional","affiliation":[{"name":"Research Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Universit\u00e0 Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Angeletti","sequence":"additional","affiliation":[{"name":"Research Unit of Clinical Laboratory Science, Department of Medicine and Surgery, Universit\u00e0 Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy"},{"name":"Operative Research Unit of Laboratory, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s00134-012-2769-8","article-title":"Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012","volume":"39","author":"Dellinger","year":"2013","journal-title":"Intensive Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Husab\u00f8, G., Nilsen, R.M., Flaatten, H., Sollig\u00e5rd, E., Frich, J.C., Bondevik, G.T., Braut, G.S., Walshe, K., Harthug, S., and Hovlid, E. 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