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System Integration Framework for Implementing a Machine Learning-Driven Clinical Decision Support System in Emergency Departments

Published: 09 September 2024 Publication History

Abstract

This paper addresses the challenges faced by physicians in the emergency department (ED) regarding insufficient clinical decision support and the disintegration of hospital information systems (HIS). Through a case study, a system integration architecture is designed to facilitate the development of a machine learning-based clinical decision support system (CDSS) in the ED. The architecture enables seamless data access and processing. By integrating disparate HIS systems, physicians can make informed decisions by leveraging machine learning techniques. This research contributes to improving healthcare outcomes in the ED by enhancing decision-support capabilities through system integration and the application of machine learning.

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  1. System Integration Framework for Implementing a Machine Learning-Driven Clinical Decision Support System in Emergency Departments

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    ICMHI '24: Proceedings of the 2024 8th International Conference on Medical and Health Informatics
    May 2024
    349 pages
    ISBN:9798400716874
    DOI:10.1145/3673971
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 09 September 2024

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    Author Tags

    1. Clinical Decision support system
    2. Emergency department
    3. Hospital information system
    4. System integration

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