{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:41:14Z","timestamp":1773549674718,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This retrospective cohort study used EHR data and included patients with at least one admission to an inpatient unit. One admission per patient was randomly selected. Adult inpatients were 18\u00a0years or older while pediatric inpatients were more than 28 days and less than 18 years. Admissions were temporally split into training (January 1, 2008 to December 31, 2019), validation (January 1, 2020 to December 31, 2020), and test (January 1, 2021 to August 1, 2022) sets. Primary comparison was a self-supervised model trained in adult inpatients versus count-based logistic regression models trained in pediatric inpatients. Primary outcome was mean area-under-the-receiver-operating-characteristic-curve (AUROC) for 11 distinct clinical outcomes. Models were evaluated in pediatric inpatients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>When evaluated in pediatric inpatients, mean AUROC of self-supervised model trained in adult inpatients (0.902) was noninferior to count-based logistic regression models trained in pediatric inpatients (0.868) (mean difference\u2009=\u20090.034, 95% CI=0.014-0.057; P\u2009&amp;lt;\u2009.001 for noninferiority and P\u2009=\u2009.006 for superiority).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Self-supervised learning in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients. This finding suggests transferability of self-supervised models trained in adult patients to pediatric patients, without requiring costly model retraining.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocad175","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T19:37:35Z","timestamp":1693251455000},"page":"2004-2011","source":"Crossref","is-referenced-by-count":10,"title":["Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks"],"prefix":"10.1093","volume":"30","author":[{"given":"Joshua","family":"Lemmon","sequence":"first","affiliation":[{"name":"Program in Child Health Evaluative Sciences, The Hospital for Sick Children , Toronto, ON M5G1X8, Canada"}]},{"given":"Lin Lawrence","family":"Guo","sequence":"additional","affiliation":[{"name":"Program in Child Health Evaluative Sciences, The Hospital for Sick Children , Toronto, ON M5G1X8, Canada"}]},{"given":"Ethan","family":"Steinberg","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University , Palo Alto, CA 94305, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8307-1642","authenticated-orcid":false,"given":"Keith E","family":"Morse","sequence":"additional","affiliation":[{"name":"Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University , Palo Alto, CA 94304, United States"}]},{"given":"Scott Lanyon","family":"Fleming","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University , Palo Alto, CA 94305, United States"}]},{"given":"Catherine","family":"Aftandilian","sequence":"additional","affiliation":[{"name":"Division of Hematology\/Oncology, Department of Pediatrics, Stanford University , Palo Alto, CA 94304, United States"}]},{"given":"Stephen R","family":"Pfohl","sequence":"additional","affiliation":[{"name":"Google Research , Mountain View, CA 94043, United States"}]},{"given":"Jose D","family":"Posada","sequence":"additional","affiliation":[{"name":"Universidad del Norte , Barranquilla 081007, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9385-7158","authenticated-orcid":false,"given":"Nigam","family":"Shah","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University , Palo Alto, CA 94305, United States"}]},{"given":"Jason","family":"Fries","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University , Palo Alto, CA 94305, United States"}]},{"given":"Lillian","family":"Sung","sequence":"additional","affiliation":[{"name":"Program in Child Health Evaluative Sciences, The Hospital for Sick Children , Toronto, ON M5G1X8, Canada"},{"name":"Division of Haematology\/Oncology, The Hospital for Sick 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