학술논문
Machine Learning and Health Science Research: Tutorial.
Document Type
Academic Journal
Author
Cho H; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; She J; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; De Marchi D; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; El-Zaatari H; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Barnes EL; Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Center for Gastrointestinal Biology and Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Kahkoska AR; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Kosorok MR; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Virkud AV; Kidney Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Source
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Subject
Language
English
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.
(©Hunyong Cho, Jane She, Daniel De Marchi, Helal El-Zaatari, Edward L Barnes, Anna R Kahkoska, Michael R Kosorok, Arti V Virkud. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.01.2024.)
(©Hunyong Cho, Jane She, Daniel De Marchi, Helal El-Zaatari, Edward L Barnes, Anna R Kahkoska, Michael R Kosorok, Arti V Virkud. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.01.2024.)