Machine Learning to Improve Decision Support for Preventing Adverse Drug Events.

Journal: Studies in health technology and informatics
Published Date: May 15, 2025

Abstract

One approach to preventing adverse drug events (ADEs), such as harmful drug interactions, is the implementation of clinical decision support systems (CDSS). In an ongoing project, we are investigating the accuracy of the rule-based CDSS currently utilized in Swedish healthcare for predicting ADEs and exploring whether machine learning (ML) can improve these predictions. By analyzing real-world healthcare data from a Swedish region spanning a 10-year period, we show that ML has potential to improve ADE predictions compared to existing rule-based CDSS.

Authors

  • Tora Hammar
    E-health Institute, Department of Medicine and Optometry, Linnaeus University , Kalmar, Sweden.
  • Daniel Nilsson
    Linnaeus University Centre for Data Intensive Sciences and Applications (LnuC DISA), Department of Computer science and Media technology (CM), Faculty of Technology, Linnaeus University, Sweden.
  • Olof Björneld
    LnuC DISA, Department of Computer science and Media technology (CM), Faculty of Technology, Linnaeus University, Sweden.
  • Celina Sving
    Department of Pharmacy, Uppsala University, Uppsala, Sweden.
  • Alisa Lincke
    Faculty of Technology, Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden.

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