Electronic Healthcare Quality Inference

Our lab conducts interdisciplinary research with a focus on utilizing advanced computational methods on electronic healthcare records to assess and enhance the quality of healthcare delivery. By employing natural language processing techniques, we extract meaningful clinical information from unstructured text in health records to support decision-making and quality assessment. Additionally, we deploy federated learning strategies enabling collaboration across multiple institutions, while respecting patient privacy and data security.


A Multicenter Evaluation of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations


Multinational Patterns of Second-line Anti-hyperglycemic Drug Initiation in Established Cardiovascular Disease: a Federated Pharmacoepidemiologic Evaluation in LEGEND-T2DM


Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction