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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Machine Learning (1465) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Electronic Health Records (1053) Clinical Informatics (1022)

Published on in Vol 10, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37833, first published .
Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study

Authors of this article:

Lara J Kanbar1 Author Orcid Image ;   Benjamin Wissel2 Author Orcid Image ;   Yizhao Ni2, 3 Author Orcid Image ;   Nathan Pajor1, 2, 3 Author Orcid Image ;   Tracy Glauser3, 4 Author Orcid Image ;   John Pestian2, 3 Author Orcid Image ;   Judith W Dexheimer2, 3, 5 Author Orcid Image
Article Authors Cited by (11) Tweetations (12) Metrics

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  2. Wissel B, Greiner H, Glauser T, Mangano F, Holland‐Bouley K, Zhang N, Szczesniak R, Santel D, Pestian J, Dexheimer J. Automated, machine learning–based alerts increase epilepsy surgery referrals: A randomized controlled trial. Epilepsia 2023;64(7):1791 View
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  8. Martinson A, Chin A, Butte M, Rider N. Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise. The Journal of Allergy and Clinical Immunology: In Practice 2024;12(10):2695 View
  9. Yuan H. Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials. Health Care Science 2024;3(5):360 View
  10. Nguyen K, Wilson D, Diiulio J, Hall B, Militello L, Gellad W, Harle C, Lewis M, Schmidt S, Rosenberg E, Nelson D, He X, Wu Y, Bian J, Staras S, Gordon A, Cochran J, Kuza C, Yang S, Lo-Ciganic W. Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings. Bioelectronic Medicine 2024;10(1) View