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Editorial

. 2023 Mar;98(3):366-369.

doi: 10.1016/j.mayocp.2023.01.013.

Implementing Machine Learning in the Electronic Health Record: Checklist of Essential Considerations

Kensaku Kawamoto¹, Joseph Finkelstein², Guilherme Del Fiol³

Affiliations

Affiliations

¹ Department of Biomedical Informatics, University of Utah, Salt Lake City, UT. Electronic address: kensaku.kawamoto@utah.edu.
² Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
³ Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.

PMID: 36868743
DOI: 10.1016/j.mayocp.2023.01.013

Editorial

Implementing Machine Learning in the Electronic Health Record: Checklist of Essential Considerations

Kensaku Kawamoto et al. Mayo Clin Proc. 2023 Mar.

. 2023 Mar;98(3):366-369.

doi: 10.1016/j.mayocp.2023.01.013.

Authors

Kensaku Kawamoto¹, Joseph Finkelstein², Guilherme Del Fiol³

Affiliations

¹ Department of Biomedical Informatics, University of Utah, Salt Lake City, UT. Electronic address: kensaku.kawamoto@utah.edu.
² Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
³ Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.

PMID: 36868743
DOI: 10.1016/j.mayocp.2023.01.013

No abstract available

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Comment on

A Clinician's Guide to Running Custom Machine-Learning Models in an Electronic Health Record Environment.
Ryu AJ, Ayanian S, Qian R, Core MA, Heaton HA, Lamb MW, Parikh RS, Boyum JP, Garza EL, Condon JL, Peters SG. Ryu AJ, et al. Mayo Clin Proc. 2023 Mar;98(3):445-450. doi: 10.1016/j.mayocp.2022.11.019. Mayo Clin Proc. 2023. PMID: 36868752

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