Duke Health Distinguished Professor of Biostatistics & Bioinformatics
David Page works on algorithms for data mining and machine learning, as well as their applications to biomedical data, especially de-identified electronic health records and high-throughput genetic and other molecular data. Of particular interest are machine learning methods for complex multi-relational data (such as electronic health records or molecules as shown) and irregular temporal data, and methods that find causal relationships or produce human-interpretable output (such as the rules for molecular bioactivity shown in green to the side).
Appointments and Affiliations
- Duke Health Distinguished Professor of Biostatistics & Bioinformatics
- Professor of Biostatistics & Bioinformatics
- Professor of Computer Science
- Chair of Biostatistics & Bioinformatics
Contact Information
- Office Location: 2424 Erwin Road Suite 1102, 11072 Hock Plaza, Durham, NC 27705
- Email Address: david.page@duke.edu
- Websites:
Education
- Ph.D. University of Illinois, 1993
Awards, Honors, and Distinctions
- Fellow of ACMI 2021. American Medical Informatics Association. 2021
Courses Taught
- BIOSTAT 824: Case Studies in Biomedical Data Science
- BIOSTAT 823: Statistical Program for Big Data
In the News
- Duke Awards 44 Distinguished Professorships (May 4, 2023 | Duke Today)
- ChatGPT Is Here to Stay. What Do We Do With It? (Feb 21, 2023 | Duke Today)
Representative Publications
- Liu, Peng, David Page, Paul Ahlquist, Irene M. Ong, and Anthony Gitter. “MPAC: a computational framework for inferring pathway activities from multi-omic data.” Bioinformatics 41, no. 10 (October 2, 2025). https://doi.org/10.1093/bioinformatics/btaf490.
- Stuebe, Alison M., Randall Blanco, Michael Horvath, Mohammad Golam Kibria, Lauren Kucirka, Karl Shieh, David Page, Metin N. Gurcan, and William Ed Hammond. “Development and implementation of an entity relationship diagram for perinatal data.” JAMIA Open 8, no. 5 (October 2025): ooaf117. https://doi.org/10.1093/jamiaopen/ooaf117.
- Vasudevan, Lavanya, Mohammad Golam Kibria, Lauren M. Kucirka, Karl Shieh, Mian Wei, Safoora Masoumi, Subha Balasubramanian, et al. “Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.” J Med Internet Res 27 (August 14, 2025): e68225. https://doi.org/10.2196/68225.
- Hokanson, James A., John O. L. DeLancey, Anna C. Kirby, Brenda Gillespie, H Henry Lai, Karl J. Kreder, C Emi Bretschneider, et al. “Expanded Physiological Testing of the Lower Urinary Tract in Asymptomatic Women and Those With Urgency Urinary Incontinence: Findings From the LURN-Organ Study.” Neurourol Urodyn 44, no. 5 (June 2025): 987–96. https://doi.org/10.1002/nau.70038.
- Ippolito, Giulia M., Kimberly Kenton, Catherine S. Bradley, Ting Lu, Brian Bieber, J Quentin Clemens, Anna C. Kirby, et al. “Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN II) Urinary Urgency Phenotyping Study: Methods and Baseline Urinary Symptoms by Age and Sex.” Neurourol Urodyn 44, no. 5 (June 2025): 1007–21. https://doi.org/10.1002/nau.70044.