MoCab: A framework for the deployment of machine learning models across health information systems.

Journal: Computer methods and programs in biomedicine
PMID: 39079482

Abstract

BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others.

Authors

  • Zhe-Ming Kuo
    Department of Information Management, National Central University, Taoyuan, Taiwan.
  • Kuan-Fu Chen
    College of Intelligent Computing, Chang Gung University, Taoyuan, Taiwan; Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan; Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan.
  • Yi-Ju Tseng
    Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.

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