Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench - Jonathan R. Holt; Stefanee Tillman; Javan Carter; Edward Preble; Sheryl C. Cates; Daniel Brannock; Michael Long; John McCarthy; Leslie Zapata Leiva; Jamboor K. Vishwanatha; Toufeeq Syed; Legand Burge; et al. (Data Science in Science, 2025)
Machine learning is revolutionizing health research by enabling scalable analysis across complex datasets. The All of Us Research Program offers unprecedented access to a wealth of health data. To harness this potential, researchers must navigate the All of Us database structure, develop machine learning skills, and apply coding effectively. This paper presents case studies designed to impart these skills using the All of Us Researcher Workbench. Our case studies cover critical topics, such as dataset selection, data cleaning, machine learning applications, and visualization in Python, which together provide the foundation of a targeted training program. Evaluated through pre- and post-program surveys, the program significantly boosted participants’ machine learning competencies. By detailing our approach and findings, we aim to guide researchers in harnessing the full potential of the All of Us dataset, thereby advancing precision medicine.