This paper is in the following e-collection/theme issue:

Ontologies, Classifications, and Coding (308) Electronic Health Records (1269) Secondary Use of Clinical Data for Research and Surveillance (452) Natural Language Processing (868) Machine Learning (1965) Big Data (304) Tools, Programs and Algorithms (357)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39057, first published .
Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care–Associated Infection

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

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

Authors of this article:

Amber C Kiser1 Author Orcid Image ;   Karen Eilbeck1 Author Orcid Image ;   Jeffrey P Ferraro2 Author Orcid Image ;   David E Skarda3, 4 Author Orcid Image ;   Matthew H Samore2, 5 Author Orcid Image ;   Brian Bucher1, 4 Author Orcid Image
Article Authors Cited by (4) Tweetations (9) Metrics

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  3. Wang Y, Lei S, Liu B. Named Entity Recognition Model with Counterfactual Mechanism to Enhance Vocabulary. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 2025;49(3):850 View
  4. Detrois K, Hartonen T, Teder-Laving M, Jermy B, Läll K, Yang Z, Mägi R, Ripatti S, Ganna A. Cross-biobank generalizability and accuracy of electronic health record-based predictors compared to polygenic scores. Nature Genetics 2025;57(9):2136 View