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

Reviews in Medical Informatics (82) Standards and Interoperability (60) Machine Learning (1370) Ontologies, Classifications, and Coding (223) Standards and Interoperability (60) Clinical Informatics (904) Clinical Information and Decision Making (1314) Machine Learning (1370) Ontologies, Classifications, and Coding (223) Standards and Interoperability (60) Clinical Informatics (904) Clinical Information and Decision Making (1314) Machine Learning (1370) Clinical Informatics (904) Clinical Information and Decision Making (1314) Ontologies, Classifications, and Coding (223)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48297, first published .
Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Authors of this article:

Jeremy A Balch1, 2 Author Orcid Image ;   Matthew M Ruppert2, 3 Author Orcid Image ;   Tyler J Loftus1, 2 Author Orcid Image ;   Ziyuan Guan2, 3 Author Orcid Image ;   Yuanfang Ren2, 3 Author Orcid Image ;   Gilbert R Upchurch1 Author Orcid Image ;   Tezcan Ozrazgat-Baslanti2, 3 Author Orcid Image ;   Parisa Rashidi2, 4 Author Orcid Image ;   Azra Bihorac2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  2. Wenk J, Voigt I, Inojosa H, Schlieter H, Ziemssen T. Building digital patient pathways for the management and treatment of multiple sclerosis. Frontiers in Immunology 2024;15 View