Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection.

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Bibliographic Details
Title:	Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection.
Authors:	Kiser AC; Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States., Eilbeck K; Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States., Ferraro JP; Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States., Skarda DE; Center for Value-Based Surgery, Intermountain Healthcare, Salt Lake City, UT, United States.; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States., Samore MH; Department of Medicine, School of Medicine, University of Utah, Salt Lake City, UT, United States.; Informatics, Decision-Enhancement and Analytic Sciences Center 2.0, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, United States., Bucher B; Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, United States.; Department of Surgery, School of Medicine, University of Utah, Salt Lake City, UT, United States.
Source:	JMIR medical informatics [JMIR Med Inform] 2022 Aug 30; Vol. 10 (8), pp. e39057. Date of Electronic Publication: 2022 Aug 30.
Publication Type:	Journal Article
Journal Info:	Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101645109 Publication Model: Electronic Cited Medium: Print ISSN: 2291-9694 (Print) Linking ISSN: 22919694 NLM ISO Abbreviation: JMIR Med Inform Subsets: PubMed not MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2291-9694
DOI:	10.2196/39057