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MoCab: A Framework for the Deployment of Machine Learning Models across Health Information Systems
  • Zhe-Ming Kuo,
  • Kuan-Fu Chen,
  • Yi-Ju Tseng
Zhe-Ming Kuo
Kuan-Fu Chen
Yi-Ju Tseng

Corresponding Author:[email protected]

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Abstract

Machine learning models are increasingly vital for clinical decision making. However, the integration of these models into health information systems (HISs) poses significant challenges, mainly due to the disparate formats of electronic health records (EHR) in various healthcare facilities. In response to this challenge, 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. MoCab simplifies the deployment process by importing and configuring saved prediction models, facilitating patient data retrieval from FHIR servers, and transmitting it to the prediction model for analysis. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts and uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop Apps for displaying laboratory results. Finally, MoCab offers the ability to continuously fine-tune and enhance the performance of deployed models over time. We demonstrate MoCab's efficacy through the successful integration of three model types, highlighting its potential in streamlining decision making amidst heterogeneous EHRs. Our proposed MoCab framework not only promotes the reusability of machine learning models across multiple EHRs but also contributes to improving clinical decision making, offering a promising solution to the challenges of integrating machine learning models into healthcare settings.
27 Dec 2023Submitted to TechRxiv
02 Jan 2024Published in TechRxiv