Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC).

To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines.

A gene-based association method for mapping traits using reference transcriptome data.

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype.

Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.

To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications.

Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).

A prognostic model based on readily available clinical data enriched a preemptive pharmacogenetic testing program.

We describe the development, implementation, and evaluation of a model to preemptively select patients for genotyping based on medication exposure risk.

"Understanding" medical school curriculum content using KnowledgeMap.

To describe the development and evaluation of computational tools to identify concepts within medical curricular documents, using information derived from the National Library of Medicine's Unified Medical Language System (UMLS). The long-term goal of the KnowledgeMap (KM) project is to provide faculty and students with an improved ability to develop, review, and integrate components of the medical school curriculum.

The KnowledgeMap project: development of a concept-based medical school curriculum database.

We developed the KnowledgeMap (KM) system as an online, concept-based database of medical school curriculum documents. It uses the KM concept indexer to map full-text documents and match search queries to concepts in the Unified Medical Language System (UMLS). In this paper, we describe the design of KM and report the first seven months of its implementation into a medical school. Despite being emphasized in only two first year courses and one fourth year course, students from all four classes used KM to search and browse documents.

Formative evaluation to guide early deployment of an online content management tool for medical curriculum.

KM is a Web-accessible, comprehensive database that organizes course materials (at the level of full lectures, not just outlines or syllabi) from the Vanderbilt School of Medicine curriculum. KM uses natural language processing techniques to analyze educational documents for biomedical concepts. Lecture handouts and Microsoft PowerPoint presentations are indexed and available online for students, faculty and administrators to search for individual or interrelated concepts across the medical school curriculum.