Machine Learning and Health Science Research: Tutorial
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.
Authors
- Helal El-Zaatari (ORCID: https://orcid.org/0000-0003-0617-1559)
- Anna R. Kahkoska (ORCID: https://orcid.org/0000-0003-2701-101X)
- Jane She (ORCID: https://orcid.org/0000-0001-8488-4357)
- Hunyong Cho (ORCID: https://orcid.org/0000-0002-0087-0079)
- Michael R. Kosorok (ORCID: https://orcid.org/0000-0002-6070-9738)
- Daniel de Marchi (ORCID: https://orcid.org/0000-0002-9222-8840)
- Arti Virkud (ORCID: https://orcid.org/0000-0003-0673-5780)
- Edward L. Barnes (ORCID: https://orcid.org/0000-0001-9894-8796)
Institutions
- University of North Carolina at Chapel Hill (US)
Publication Details
- Journal
- UNC Libraries
- Published
- 2024-10-05
- DOI
- https://doi.org/10.17615/sby7-8z63
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Diabetes Association
- Diabetes Research Connection
- National Institutes of Health
- National Center for Advancing Translational Sciences