{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:55Z","timestamp":1747216195685,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and under-dosing, based on anti-Xa results, using a monocentric retrospective dataset. The random forest model achieved a mean AUROC of 0.80 [0.77-0.83], while the XGB model reached a mean AUROC of 0.80 [0.76-0.83]. Feature importance was employed to enhance the interpretability of the model, a critical factor for clinician acceptance. After prospective validation, machine learning models such as those developed in this study could be implemented within a computerized physician order entry (CPOE) as a clinical decision support system (CDSS).<\/jats:p>","DOI":"10.3233\/shti240763","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:38:54Z","timestamp":1724409534000},"source":"Crossref","is-referenced-by-count":0,"title":["Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6055-6935","authenticated-orcid":false,"given":"Boris","family":"Delange","sequence":"first","affiliation":[{"name":"CHU Rennes, INSERM, LTSI-UMR 1099, Univ Rennes, 35000 Rennes, France"},{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, H\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, 2, rue Henri Le Guilloux, 35033 Rennes cedex 9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Bouzille","sequence":"additional","affiliation":[{"name":"CHU Rennes, INSERM, LTSI-UMR 1099, Univ Rennes, 35000 Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabelle","family":"Gouin","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019H\u00e9matologie-H\u00e9mostase, Centre Hospitalo-Universitaire de Rennes, IRSET, UMR_S 1085, F35000, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoann","family":"Launey","sequence":"additional","affiliation":[{"name":"D\u00e9partement Anesth\u00e9sie R\u00e9animation et M\u00e9decine P\u00e9riop\u00e9ratoire, Centre Hospitalier Universitaire (CHU) Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"Mansour","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019H\u00e9matologie-H\u00e9mostase, Centre Hospitalo-Universitaire de Rennes, IRSET, UMR_S 1085, F35000, Rennes, France"},{"name":"D\u00e9partement Anesth\u00e9sie R\u00e9animation et M\u00e9decine P\u00e9riop\u00e9ratoire, Centre Hospitalier Universitaire (CHU) Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Cuggia","sequence":"additional","affiliation":[{"name":"CHU Rennes, INSERM, LTSI-UMR 1099, Univ Rennes, 35000 Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adel","family":"Maamar","sequence":"additional","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, H\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, 2, rue Henri Le Guilloux, 35033 Rennes cedex 9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240763","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:38:55Z","timestamp":1724409535000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240763","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}