{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:23:04Z","timestamp":1769300584435,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682648","type":"print"},{"value":"9781643682655","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"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":[[2022,6,6]]},"abstract":"<jats:p>Objective: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. Materials: This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014\u20132018 at a quaternary children\u2019s hospital. Methods: We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. Results: The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 \u223c 0.956). Conclusions: Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.<\/jats:p>","DOI":"10.3233\/shti220160","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:15Z","timestamp":1654594395000},"source":"Crossref","is-referenced-by-count":4,"title":["Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records"],"prefix":"10.3233","author":[{"given":"Lingyun","family":"Shi","sequence":"first","affiliation":[{"name":"Tsui Laboratory, Children\u2019s Hospital of Philadelphia (CHOP)"},{"name":"Department of Biomedical Informatics, CHOP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveen","family":"Muthu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, CHOP"},{"name":"University of Pennsylvania Perelman School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerald P.","family":"Shaeffer","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, CHOP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsui Laboratory, Children\u2019s Hospital of Philadelphia (CHOP)"},{"name":"Department of Biomedical Informatics, CHOP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor M.","family":"Ruiz Herrera","sequence":"additional","affiliation":[{"name":"Tsui Laboratory, Children\u2019s Hospital of Philadelphia (CHOP)"},{"name":"Department of Biomedical Informatics, CHOP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuchiang R.","family":"Tsui","sequence":"additional","affiliation":[{"name":"Tsui Laboratory, Children\u2019s Hospital of Philadelphia (CHOP)"},{"name":"Department of Biomedical Informatics, CHOP"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220160","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:16Z","timestamp":1654594396000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"ISBN":["9781643682648","9781643682655"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220160","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}