Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Machine Learning Approaches for Drug Adverse Event Detection: Utilizes machine learning algorithms to detect adverse events associated with drugs from real-world data

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  • Dr. Thomas Müller
Dr. Thomas Müller
Associate Professor of Bioinformatics, University of Vienna, Austria
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Published 08-06-2024

Keywords

  • Machine Learning,
  • Drug Adverse Events,
  • Real-World Data,
  • Healthcare,
  • Natural Language Processing,
  • Supervised Learning,
  • Unsupervised Learning,
  • Data Quality,
  • Bias
    • ...More
    Less
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Thomas Müller, “Machine Learning Approaches for Drug Adverse Event Detection: Utilizes machine learning algorithms to detect adverse events associated with drugs from real-world data”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 78–85, Jun. 2024, Accessed: Sep. 19, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/22

Abstract

This research paper explores the application of machine learning (ML) algorithms for detecting adverse events related to drugs using real-world data. Adverse drug events (ADEs) are a significant concern in healthcare, often leading to patient morbidity and mortality. Traditional methods of ADE detection rely heavily on manual reporting, which can be limited in scope and accuracy. ML offers a promising approach to enhance ADE detection by analyzing vast amounts of diverse data sources. This paper discusses various ML techniques, including supervised and unsupervised learning, as well as natural language processing (NLP) for text mining in health records. We review the challenges, such as data quality and bias, and propose future directions for improving ADE detection using ML.

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