Overview
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).
Current Appointments & Affiliations
Duke Health Distinguished Professor of Biostatistics & Bioinformatics
·
2025 - Present
Biostatistics & Bioinformatics, Division of Biostatistics,
Biostatistics & Bioinformatics
Professor of Biostatistics & Bioinformatics
·
2021 - Present
Biostatistics & Bioinformatics, Division of Biostatistics,
Biostatistics & Bioinformatics
Professor of Computer Science
·
2021 - Present
Computer Science,
Trinity College of Arts & Sciences
Chair of Biostatistics & Bioinformatics
·
2019 - Present
Biostatistics & Bioinformatics, Division of Biostatistics,
Biostatistics & Bioinformatics
Recent Publications
MPAC: a computational framework for inferring pathway activities from multi-omic data.
Journal Article Bioinformatics · October 2, 2025 MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurement ... Full text Link to item CiteDevelopment and implementation of an entity relationship diagram for perinatal data.
Journal Article JAMIA Open · October 2025 OBJECTIVE: Severe maternal morbidity and mortality are higher in the United States than in other high-income countries, and unacceptable disparities persist. To facilitate research on these outcomes, we developed a standardized approach for extracting peri ... Full text Link to item CiteMachine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.
Journal Article J Med Internet Res · August 14, 2025 BACKGROUND: A majority (>80%) of maternal deaths in the United States are preventable. Using machine learning (ML) models that are generated from electronic medical records (EMRs) may be a promising approach to predict the risk of adverse maternal outcomes ... Full text Link to item CiteRecent Grants
Advancing a Holistic Understanding of Variability in Lived Experience with Sickle Cell Pain
ResearchCo-Principal Investigator · Awarded by National Institutes of Health · 2025 - 2030Deprescribing Decision-Making using Machine Learning Individualized Treatment Rules to Improve CNS Polypharmacy
ResearchCo Investigator · Awarded by National Institutes of Health · 2024 - 2029Integrated Detection and Classification of Sepsis via Tensor Methods Using EHR
ResearchCo Investigator · Awarded by National Heart, Lung, and Blood Institute · 2024 - 2029View All Grants
Education, Training & Certifications
University of Illinois ·
1993
Ph.D.