Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench
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 lea...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Data Science in Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2025.2523871 |
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| author | 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 Robert T. Mallet Shelly Kowalczyk Jennifer D. Uhrig Megan A. Lewis |
| author_facet | 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 Robert T. Mallet Shelly Kowalczyk Jennifer D. Uhrig Megan A. Lewis |
| author_sort | Jonathan R. Holt |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e3484e3a01e142f28d8a3c03ea9c3f6b |
| institution | Matheson Library |
| issn | 2694-1899 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Data Science in Science |
| spelling | doaj-art-e3484e3a01e142f28d8a3c03ea9c3f6b2025-07-03T18:02:37ZengTaylor & Francis GroupData Science in Science2694-18992025-12-014110.1080/26941899.2025.2523871Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher WorkbenchJonathan R. Holt0Stefanee Tillman1Javan Carter2Edward Preble3Sheryl C. Cates4Daniel Brannock5Michael Long6John McCarthy7Leslie Zapata Leiva8Jamboor K. Vishwanatha9Toufeeq Syed10Legand Burge11Robert T. Mallet12Shelly Kowalczyk13Jennifer D. Uhrig14Megan A. Lewis15Center for Data Science and AI, RTI International, RTP, NC, USAClinical Research, RTI International, RTP, NC, USAGenOmics, RTI International, RTP, NC, USACenter for Data Science and AI, RTI International, RTP, NC, USACenter for Communication and Engagement Research, RTI International, RTP, NC, USACenter for Data Science and AI, RTI International, RTP, NC, USACenter for Data Science and AI, RTI International, RTP, NC, USACenter for Data Science and AI, RTI International, RTP, NC, USACenter for Communication and Engagement Research, RTI International, RTP, NC, USAInstitute for Health Disparities, The University of North Texas Health Science Center, Fort Worth, TX, USADepartment of Health Data Science and Artificial Intelligence, The University of Texas Health Science Center, Houston, TX, USADepartment of Electrical Engineering and Computer Science, Howard University, Washington, DC, USADepartment of Physiology and Anatomy, The University of North Texas Health Science Center, Fort Worth, TX, USACenter for Community Prevention and Treatment Research, The Maya Tech Corporation, Silver Spring, MD, USACenter for Data Science and AI, RTI International, RTP, NC, USACenter for Data Science and AI, RTI International, RTP, NC, USAMachine 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.https://www.tandfonline.com/doi/10.1080/26941899.2025.2523871All of Us Researcher Workbenchmachine learningartificial intelligenceelectronic health recordstraining programprofessional development |
| spellingShingle | 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 Robert T. Mallet Shelly Kowalczyk Jennifer D. Uhrig Megan A. Lewis Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench Data Science in Science All of Us Researcher Workbench machine learning artificial intelligence electronic health records training program professional development |
| title | Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench |
| title_full | Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench |
| title_fullStr | Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench |
| title_full_unstemmed | Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench |
| title_short | Enhancing Health Research with Machine Learning: Practical Case Studies Using the All of Us Researcher Workbench |
| title_sort | enhancing health research with machine learning practical case studies using the all of us researcher workbench |
| topic | All of Us Researcher Workbench machine learning artificial intelligence electronic health records training program professional development |
| url | https://www.tandfonline.com/doi/10.1080/26941899.2025.2523871 |
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