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Machine Learning and Health Science Research: A Tutorial
Jane She;
Hunyong Cho;
Daniel De Marchi;
Helal El-Zaatari;
Edward L. Barnes;
Anna R. Kahkoska;
Arti V. Virkud;
Michael R. Kosorok
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 as well as a brief primer and extra details in the supplement. This paper will touch on not only ML algorithms, their capabilities, and incorporating them into research, but also provides practical use cases from domains such as imaging data analysis, natural language processing, genomics, and some precision medicine methodologies.
Citation
Please cite as:
She J, Cho H, De Marchi D, El-Zaatari H, Barnes EL, Kahkoska AR, Virkud AV, Kosorok MR
Machine Learning and Health Science Research: Tutorial