{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:21:55Z","timestamp":1756311715769,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684505"},{"type":"electronic","value":"9781643684512"}],"license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"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":[[2023,10,20]]},"abstract":"<jats:p>Overcrowding in EDs has been viewed globally as a chronic health challenge. It is directly related to the increased use of EDs for non-urgent issues, leading to increased complications, long waiting times, a higher death rate, or delayed intervention of those more acutely ill. This study aims to develop Machine Learning models to differentiate immediate medical needs from unnecessary ED visits. A Decision Tree, Random Forest, AdaBoost, and XGBoost models were built and evaluated on real-life data. XGBoost achieved the best accuracy and F1-score.<\/jats:p>","DOI":"10.3233\/shti230747","type":"book-chapter","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:16:22Z","timestamp":1698048982000},"source":"Crossref","is-referenced-by-count":1,"title":["Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8428-3092","authenticated-orcid":false,"given":"Sarah A.","family":"Alkhodair","sequence":"first","affiliation":[{"name":"IT Department, CCIS, King Saud University, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0119-5933","authenticated-orcid":false,"given":"Norah","family":"Altwaijri","sequence":"additional","affiliation":[{"name":"IT Department, CCIS, King Saud University, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3246-454X","authenticated-orcid":false,"given":"Ahmed I.","family":"Albarrak","sequence":"additional","affiliation":[{"name":"Medical Informatics and E-Learning Unit, Medical Education Department, RCHIP, College of Medicine, King Saud University, Riyadh, Saudi Arabia"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Telehealth Ecosystems in Practice"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:16:23Z","timestamp":1698048983000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,20]]},"ISBN":["9781643684505","9781643684512"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230747","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,10,20]]}}}