Deep Phenotyping
A series of ongoing investigations focus on multimodality digital health data to evaluate the phenotypic variability of patients with cardiovascular disease and the quality of care they receive
Precision Diagnosis and Therapy
We are developing tools that personalize the assessment of randomized clinical trials through the application of advanced data science and machine learning
Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes
A Phenomapping-derived Tool to Personalize
the Selection of Anatomical vs. Functional Testing
in Evaluating Chest Pain (ASSIST)
Digital Phenotyping
Our work increasingly focuses on incorporating structured and unstructured clinical data into diagnostic and prognostic tools, and in the measurement of health quality.
Electronic Clinical Quality Measurement
Using structured and unstructured data from the Electronic Health Record, we are defining the quality of care received by patients following hospitalization for heart failure and acute myocardial infarction
Medical Natural Language Processing
Extracting phenotypic features and risk prediction from clinical documentation - harnessing the power of advanced medical NLP
Clinical Signal Processing
Analyses of signal data including electrocardiograms, telemetry, and other clinical signals
Cardiovascular Computer Vision
Identifying radiomic biomarkers of cardiovascular health and disease