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.
Keywords: health science researcher; machine learning; machine learning pipeline; medical machine learning; precision medicine; reproducibility; unsupervised learning.
©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.