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

Ontologies, Classifications, and Coding (239) Electronic Health Records (1053) Secondary Use of Clinical Data for Research and Surveillance (367) Natural Language Processing (693) Machine Learning (1465) Big Data (240) Tools, Programs and Algorithms (266)

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 (2) Tweetations (10) Metrics

Journals

  1. Kiser A, Shi J, Bucher B. An explainable long short-term memory network for surgical site infection identification. Surgery 2024;176(1):24 View
  2. Shen L, An J, Wang N, Wu J, Yao J, Gao Y. Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis. World Journal of Urology 2024;42(1) View