Enhancing Health Research with Machine Learning: Practical Case Studies Using the <i>All of Us</i> Researcher Workbench

Abstract

Machine learning is revolutionizing health research by enabling scalable analysis across complex datasets. The <i>All of Us</i> Research Program offers unprecedented access to a wealth of health data. To harness this potential, researchers must navigate the <i>All of Us</i> database structure, develop machine learning skills, and apply coding effectively. This paper presents case studies designed to impart these skills using the <i>All of Us</i> 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 <i>All of Us</i> dataset, thereby advancing precision medicine.

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