{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:27Z","timestamp":1747216167064,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684567"},{"type":"electronic","value":"9781643684574"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"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":[[2024,1,25]]},"abstract":"<jats:p>Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predictors of delirium were different from those of self-extubation after delirium. Data-driven decisions, rather than empirical decisions, on whether or not to use physical restraints or other actions that cause patient suffering will result in improved value in medical care.<\/jats:p>","DOI":"10.3233\/shti231115","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:26:18Z","timestamp":1706178378000},"source":"Crossref","is-referenced-by-count":1,"title":["Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0769-7763","authenticated-orcid":false,"given":"Koutarou","family":"Matsumoto","sequence":"first","affiliation":[{"name":"Biostatistics Center, Kurume University, Japan"},{"name":"Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Japan"}]},{"given":"Yasunobu","family":"Nohara","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Japan"},{"name":"Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Japan"}]},{"given":"Mikako","family":"Sakaguchi","sequence":"additional","affiliation":[{"name":"Department of Nursing, Saiseikai Kumamoto Hospital, Japan"}]},{"given":"Yohei","family":"Takayama","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Japan"},{"name":"Department of Nursing, Saiseikai Kumamoto Hospital, Japan"}]},{"given":"Takanori","family":"Yamashita","sequence":"additional","affiliation":[{"name":"Medical Information Center, Kyushu University Hospital, Japan"}]},{"given":"Hidehisa","family":"Soejima","sequence":"additional","affiliation":[{"name":"Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Japan"}]},{"given":"Naoki","family":"Nakashima","sequence":"additional","affiliation":[{"name":"Medical Information Center, Kyushu University Hospital, Japan"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:26:19Z","timestamp":1706178379000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231115","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}