Overview
I study the meaningful use of Electronic Health Records data. My research interests sit at the intersection of biostatistics, biomedical informatics, machine learning and epidemiology. I collaborate with researchers both locally at Duke as well as nationally. I am interested in speaking with any students, methodologists or collaborators interested in EHR data.
Please find more information at: https://biostat.duke.edu/goldstein-lab
Please find more information at: https://biostat.duke.edu/goldstein-lab
Current Appointments & Affiliations
Professor of Biostatistics & Bioinformatics
·
2023 - Present
Biostatistics & Bioinformatics, Division of Translational Biomedical,
Biostatistics & Bioinformatics
Associate Professor in Pediatrics
·
2020 - Present
Pediatrics, Children's Health Discovery Institute,
Pediatrics
Professor in Population Health Sciences
·
2023 - Present
Population Health Sciences,
Basic Science Departments
Member in the Duke Clinical Research Institute
·
2014 - Present
Duke Clinical Research Institute,
Institutes and Centers
Recent Publications
Discrete Time Neural Network Models to Address Time-Varying Predictor Importance: An Illustration in Predicting Mortality Over Different Time Horizons.
Journal Article IEEE J Biomed Health Inform · January 2026 Clinical predictive models (CPMs) are crucial for forecasting patient outcomes using available electronic health record (EHR) data. Traditional time-to-event (TTE) models, like the Cox proportional hazards model, assume that hazard ratios remain constant o ... Full text Link to item CiteAssociation of neighborhood disadvantage with clinical and healthcare utilization outcomes following traumatic brain injury.
Journal Article J Clin Neurosci · January 2026 BACKGROUND: Health disparities in traumatic brain injury (TBI) risk and outcomes have been observed. Neighborhood-level social determinants of health (SDOH), such as built environment and socioeconomic disadvantage, may contribute to these disparities. To ... Full text Link to item CiteUsing Encounter-Level Data for Risk-Adjustment of Antimicrobial Use Comparisons: Feasibility and Variable Selection.
Journal Article Clin Infect Dis · December 15, 2025 BACKGROUND: External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial stewardship program strategy. Barriers to encounter-level modeling include feasibility of data collection an ... Full text Link to item CiteRecent Grants
1/3 CTSA UM1 at Duke University
ResearchFaculty Member · Awarded by National Institutes of Health · 2025 - 20322/3 CTSA K12 Program at Duke University
ResearchMentor · Awarded by National Institutes of Health · 2025 - 2030Leveraging Artificial Intelligence to Predict Mental Health Risk among Youth Presenting to Rural Primary Care Clinics
ResearchCo Investigator · Awarded by National Institutes of Health · 2025 - 2029View All Grants
Education, Training & Certifications
University of California, Berkeley ·
2011
Ph.D.