Research
In the area of cardiovascular disease, we are generally interested in building systematic approaches to improve population and individual health. In particular, we aim to improve prediction and diagnosis of a variety of cardiovascular diseases by applying machine learning to existing clinical data and developing new biomarker-informed strategies. Current projects include testing the utility of longitudinal biomarker measurement in the diagnosis of acute myocardial infarction, building algorithms and clinical decision support tools to improve the diagnosis of specific causes of secondary hypertension, and identifying clinical factors that predict response to antihypertensive medications.
In laboratory medicine, we are working to develop tools to robustly implement multi-test, integrative interpretations and to detect cryptic testing errors.