My research is at the intersection of AI & healthcare, with a focus on the interface between clinical workflows and predictive models. I utilize methods from the domains of clinical informatics, machine learning, and operations research. My work spans the healthcare AI lifecycle with projects advancing from model development, evaluation, technical integration, and connection with clinical workflows.
Recent Research Updates
Continuing our journey to understand Apple’s VO2 max estimation algorithm, by getting workout data.
Leveraging personal HealthKit data to evaluate and understand Apple’s VO2 max estimation algorithm.
As machine learning models become more integrated into clinical care, how can we update them without violating user expectations? We proposed a new rank-base...
New article out in Cell Reports Medicine. It is a perspective paper on incorporating AI into medical education with Drs. Cornelius A. James, Kimberly D. Lom...
Global Surgical Education. Can be found here.
Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers
Infection Control and Hospital Epidemiology. Can be found here.
Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations
Journal of the American Medical Informatics Association manuscript, can be found here.
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study
British Medical Journal. Can be found here.
Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National Cohorts
The Journal of Urology article, read here.
Comparative Assessment of a Machine Learning Model and Rectal Swab Surveillance to Predict Hospital Onset Clostridioides difficile
IDWeek Abstract. Can be found here.