{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:51Z","timestamp":1747216191538,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685274"}],"license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"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":[[2024,7,24]]},"abstract":"<jats:p>This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses\u2019 concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with a minimum 24-hour stay, excluding those receiving hospice or palliative care. Predictors included demographics, clinical characteristics, and nursing documentation frequencies related to nurses\u2019 concerns. Four models were trained with 10-fold cross-validation after adjusting class imbalance. The random forest (RF) model was identified as the best-performing, with key predictors of mortality in this model being the frequency of vital signs, the frequency of nursing note documentation, and the frequency of monitoring-related nursing notes. This suggests that predictive models using nursing records, which reflect nurses\u2019 concerns as represented by the frequency of nursing documentation, may be integrated into clinical decision support tools, potentially enhancing patient outcomes in ICUs.<\/jats:p>","DOI":"10.3233\/shti240237","type":"book-chapter","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T11:29:08Z","timestamp":1721820548000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6797-6716","authenticated-orcid":false,"given":"Yeonju","family":"Kim","sequence":"first","affiliation":[{"name":"College of Nursing and Brain Korea 21 FOUR Project, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yesol","family":"Kim","sequence":"additional","affiliation":[{"name":"College of Nursing and Brain Korea 21 FOUR Project, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mona","family":"Choi","sequence":"additional","affiliation":[{"name":"College of Nursing and Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Innovation in Applied Nursing Informatics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240237","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T11:29:09Z","timestamp":1721820549000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"ISBN":["9781643685274"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240237","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,7,24]]}}}