%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e50890
%T Machine Learning and Health Science Research: Tutorial
%A Cho,Hunyong
%A She,Jane
%A De Marchi,Daniel
%A El-Zaatari,Helal
%A Barnes,Edward L
%A Kahkoska,Anna R
%A Kosorok,Michael R
%A Virkud,Arti V
%+ Department of Biostatistics, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599-7420, United States, 1 (919) 966 7250, jane.she@unc.edu
%K health science researcher
%K machine learning pipeline
%K machine learning
%K medical machine learning
%K precision medicine
%K reproducibility
%K unsupervised learning
%D 2024
%7 30.1.2024
%9 Tutorial
%J J Med Internet Res
%G English
%X 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.
%M 38289657
%R 10.2196/50890
%U https://www.jmir.org/2024/1/e50890
%U https://doi.org/10.2196/50890
%U http://www.ncbi.nlm.nih.gov/pubmed/38289657