We use machine learning, text-mining, and prior knowledge in medical ontologies to discover hidden trends, build risk models, and drive comparative effectiveness studies to enable the learning health system. Our research group is part of the Center for Biomedical Informatics Research at Stanford and the National Center for Biomedical Ontology. Our work sometimes gets covered in the popular news as well.
We use longitudinal EHR data, including unstructured data, for three groups of projects: answering clinical questions, generating insights, and building predictive models.
Learning Health System examples:
Insights from data mining:
Current Group: Lab members
Open Positions: Informatics Postdoctoral Fellow
Data Science Fellow, Health Services Research Postdoctoral Fellow
Internal (log in required): Lab information, Projects, Rotations, Archived pages
On Boarding: New Lab members, For Collaborators
BIOMEDIN 215 Data Driven Medicine Autumn quarter of each year