Matching Electronic Health Records from different sources
Problem example
An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in health care. This joining is usually resolved using meta-data, which may be unavailable or ambiguous in a large database. We design and evaluate methods for mapping features between databases independent of meta-data.
Sandhya Tripathi
Postdoctoral Research Associate
My research interests include clinical prediction model, fairness in AI models, database matching, and learning in the presence of label noise.