{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:50:57Z","timestamp":1782989457159,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>Mortality prediction in acute respiratory distress syndrome (ARDS) is critical for early triage and targeted treatment. In this study, we compared multiple machine learning models to predict 90-day all-cause mortality and 28-day hospital mortality in ARDS patients, with all models achieving good discrimination (AUC: 0.715\u20130.753 and 0.693\u20130.721, respectively). Among models, XGBoost achieved the most consistent balance of AUC, recall, and F1 score across both outcomes, making it the most clinically appropriate model where underpredicting high-risk patients brings significant consequences. Random forest models showed the highest AUC for the primary outcome but substantially lower recall.<\/jats:p>","DOI":"10.3233\/shti260878","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:01:50Z","timestamp":1782986510000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Approaches for Mortality Prediction in ARDS"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-0292","authenticated-orcid":false,"given":"Xingyue","family":"Huo","sequence":"first","affiliation":[{"name":"University of Arizona, Tucson, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Bime","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:01:50Z","timestamp":1782986510000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260878","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}