{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:28:18Z","timestamp":1771025298248,"version":"3.50.1"},"reference-count":13,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Sepsis remains a major global health concern, causing high mortality rates, prolonged hospital stays, and substantial economic burdens. The accurate prediction of clinical outcomes, such as mortality and length of stay (LOS), is critical for optimizing hospital resource allocation and improving patient management. The present study investigates the potential of machine learning (ML) models to predict these outcomes using a dataset of 1492 sepsis patients with clinical, physiological, and demographic features. After rigorous preprocessing to address missing data and ensure consistency, multiple classifiers, including Random Forest, Extra Trees, and Gradient Boosting, were trained and validated. The results demonstrate that Random Forest and Extra Trees achieve high accuracy for LOS prediction, while Gradient Boosting and Bernoulli Na\u00efve Bayes effectively predict mortality. Feature importance analysis identified ICU stay duration (ICU_DAYS_OBS) as the most influential predictor for both outcomes, alongside vital signs, white blood cell counts, and lactic acid levels. These findings highlight the potential of ML-driven clinical decision support systems (CDSSs) to enhance early risk assessment, optimize ICU resource planning, and support timely interventions. Future research should refine predictive features, integrate advanced biomarkers, and validate models across larger and more diverse datasets to improve scalability and clinical impact.<\/jats:p>","DOI":"10.3390\/computation13010008","type":"journal-article","created":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T14:35:42Z","timestamp":1735742142000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Advancements in Predictive Analytics: Machine Learning Approaches to Estimating Length of Stay and Mortality in Sepsis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9860-1729","authenticated-orcid":false,"given":"Houssem","family":"Ben Khalfallah","sequence":"first","affiliation":[{"name":"RIADI Laboratory, Ecole Nationale des Sciences de l\u2019Informatique, Manouba University, La Manouba 2010, Tunisia"},{"name":"AGEIS Laboratory, University Grenoble Alpes, 38700 La Tronche, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariem","family":"Jelassi","sequence":"additional","affiliation":[{"name":"RIADI Laboratory, Ecole Nationale des Sciences de l\u2019Informatique, Manouba University, La Manouba 2010, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8335-9240","authenticated-orcid":false,"given":"Jacques","family":"Demongeot","sequence":"additional","affiliation":[{"name":"AGEIS Laboratory, University Grenoble Alpes, 38700 La Tronche, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narj\u00e8s","family":"Bellamine Ben Saoud","sequence":"additional","affiliation":[{"name":"RIADI Laboratory, Ecole Nationale des Sciences de l\u2019Informatique, Manouba University, La Manouba 2010, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guarino, M., Perna, B., Cesaro, A.E., and Maritati, M. (2023). 2023 update on sepsis and septic shock in adult patients: Management in the emergency department. J. Clin. Med., 12.","DOI":"10.3390\/jcm12093188"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s11908-021-00765-y","article-title":"Diagnostic challenges in sepsis","volume":"23","author":"Duncan","year":"2021","journal-title":"Curr. Infect. Dis. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ccc.2011.09.003","article-title":"The economics of sepsis","volume":"28","author":"Chalupka","year":"2012","journal-title":"Crit. Care Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e3445","DOI":"10.2196\/medinform.3445","article-title":"From data to optimal decision making: A data-driven, probabilistic machine learning approach to decision support for patients with sepsis","volume":"3","author":"Tsoukalas","year":"2015","journal-title":"JMIR Med. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1377\/hlthaff.2015.1194","article-title":"National Health Spending In 2014: Faster Growth Driven By Coverage Expansion And Prescription Drug Spending","volume":"35","author":"Martin","year":"2016","journal-title":"Health Aff."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1097\/CCM.0000000000003342","article-title":"Epidemiology and Costs of Sepsis in the United States\u2014An Analysis Based on Timing of Diagnosis and Severity Level*","volume":"46","author":"Paoli","year":"2018","journal-title":"Crit. Care Med."},{"key":"ref_7","unstructured":"Kulick, D., Vaghela, S., and Bina, E. (2023, July 19). Sepsis Poses a Cost-Containment Challenge in the Face of the COVID-19 Pandemic. Healthcare Financial Management Association (HFMA). 20 July 2020. 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Rep., 12.","DOI":"10.1038\/s41598-022-17091-5"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T15:23:10Z","timestamp":1759850590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,1]]},"references-count":13,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["computation13010008"],"URL":"https:\/\/doi.org\/10.3390\/computation13010008","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,1]]}}}