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Machine Learning and Health Science Research: Tutorial.


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.

SUBMITTER: Cho H 

PROVIDER: S-EPMC10865203 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Machine Learning and Health Science Research: Tutorial.

Cho Hunyong H   She Jane J   De Marchi Daniel D   El-Zaatari Helal H   Barnes Edward L EL   Kahkoska Anna R AR   Kosorok Michael R MR   Virkud Arti V AV  

Journal of medical Internet research 20240130


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  ...[more]

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