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To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR).

To develop and validate an accurate method to identify patients with chronic pain using electronic health records (EHR) data at a multisite community health center.

Large-scale biorepositories that couple biologic specimens with electronic health records containing documentation of phenotypic expression can accelerate scientific research and discovery. However, differences between those subjects who participate in biorepository-based research and the population from which they are drawn may influence research validity. While an opt-out approach to biorepository-based research enhances inclusiveness, empirical research evaluating voluntariness, risk, and the feasibility of an opt-out approach is sparse, and factors influencing patients' [...]

Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create [...]

Many cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled.

Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.

To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).

To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction.