Machine Learning and Health Science Research: Tutorial.

Journal: Journal of medical Internet research
Published Date: Jan 30, 2024

Abstract

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Authors

  • Hunyong Cho
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Jane She
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Daniel De Marchi
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Helal El-Zaatari
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Edward L Barnes
    Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Anna R Kahkoska
    Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina.
  • Michael R Kosorok
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Arti V Virkud
    Kidney Center School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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