{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T13:45:02Z","timestamp":1772459102723,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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":[[2025,8,7]]},"abstract":"<jats:p>This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients\u2019 prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.<\/jats:p>","DOI":"10.3233\/shti250898","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:27Z","timestamp":1754566467000},"source":"Crossref","is-referenced-by-count":1,"title":["Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0267-9723","authenticated-orcid":false,"given":"Sunjin","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea"}]},{"given":"Sejin","family":"Heo","sequence":"additional","affiliation":[{"name":"Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"}]},{"given":"Sungjun","family":"Hong","sequence":"additional","affiliation":[{"name":"Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"}]},{"given":"Won Chul","family":"Cha","sequence":"additional","affiliation":[{"name":"Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea"},{"name":"Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea"}]},{"given":"Junsang","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250898","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:27Z","timestamp":1754566467000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250898","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}