The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

Journal: Journal of medical Internet research
Published Date: Aug 20, 2024

Abstract

BACKGROUND: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes.

Authors

  • Laura Swinckels
    Department of Oral Public Health, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit, Amsterdam, Netherlands.
  • Frank C Bennis
    Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands. MHeNS School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands.
  • Kirsten A Ziesemer
    University Library, Vrije Universiteit, Amsterdam, The Netherlands.
  • Janneke F M Scheerman
    Department Oral Hygiene, Cluster Health, Sports and Welfare, Inholland University of Applied Sciences, Amsterdam, Netherlands.
  • Harmen Bijwaard
    Medical Technology Research Group, Cluster Health, Sport and Welfare, Inholland University of Applied Sciences, Haarlem, Netherlands.
  • Ander de Keijzer
    Data Driven Smart Society Research Group, Faculty of Engineering, Design & Computing, Inholland University of Applied Sciences, Alkmaar, Netherlands.
  • Josef Jan Bruers
    Department of Oral Public Health, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit, Amsterdam, Netherlands.

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