• Mayo Clinic proceedings · Mar 2023

    A Clinician's Guide to Running Custom Machine-Learning Models in an Electronic Health Record Environment.

    • Alexander J Ryu, Shant Ayanian, Ray Qian, Marcia A Core, Heather A Heaton, Matthew W Lamb, Riddhi S Parikh, Jens P Boyum, Esteban L Garza, Jennifer L Condon, and Steve G Peters.
    • Mayo Clinic Division of Hospital Internal Medicine, Rochester, MN. Electronic address: Ryu.Alexander@mayo.edu.
    • Mayo Clin. Proc. 2023 Mar 1; 98 (3): 445450445-450.

    AbstractWe recently brought an internally developed machine-learning model for predicting which patients in the emergency department would require hospital admission into the live electronic health record environment. Doing so involved navigating several engineering challenges that required the expertise of multiple parties across our institution. Our team of physician data scientists developed, validated, and implemented the model. We recognize a broad interest and need to adopt machine-learning models into clinical practice and seek to share our experience to enable other clinician-led initiatives. This Brief Report covers the entire model deployment process, starting once a team has trained and validated a model they wish to deploy in live clinical operations.Copyright © 2022 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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