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We define and validate an architecture for systems that identify patient cohorts for clinical trials from multiple heterogeneous data sources. This architecture has an explicit query model capable of supporting temporal reasoning and expressing eligibility criteria independently of the representation of the data used to evaluate them.

To develop algorithms to improve efficiency of patient phenotyping using natural language processing (NLP) on text data. Of a large number of note titles available in our database, we sought to determine those with highest yield and precision for psychosocial concepts.

To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).

This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions.

To study the relation between electronic health record (EHR) variables and healthcare process events.

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.

To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).

Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia.

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

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' [...]