Dataset Information

Phenotype Information Retrieval for Existing GWAS Studies.

ABSTRACT: The database of Genotypes and Phenotypes (dbGaP) is archiving the results of different Genome Wide Association Studies (GWAS). dbGaP has a multitude of phenotype variables, but they are not harmonized across studies. We proposed a method to standardize phenotype variables by classifying similar variables based on semantic distances. We first extracted variables description, enriched them using domain knowledge, and computed the distances among them. We used clustering techniques to classify the most similar variables. We used domain experts to audit clusters, annotated the clusters with appropriate labels, and used re-clustering to build a semantically-driven Genotypes and Phenotypes (sdGaP) ontology using the UMLS semantic network and metathesaurus. The sdGaP ontology allowed us to expand user queries and retrieve information using a semantic metric called density measure (DM). We illustrated the potential improvement of information retrieval using the sdGaP ontology in one search scenario using the variables from the Cleveland Family Study.

SUBMITTER: Alipanah N

PROVIDER: S-EPMC3845737 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Phenotype Information Retrieval for Existing GWAS Studies.

Alipanah Neda N Lin Ko-Wei KW Venkatesh Vinay V Farzaneh Seena S Kim Hyeon-Eui HE

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20130318

The database of Genotypes and Phenotypes (dbGaP) is archiving the results of different Genome Wide Association Studies (GWAS). dbGaP has a multitude of phenotype variables, but they are not harmonized across studies. We proposed a method to standardize phenotype variables by classifying similar variables based on semantic distances. We first extracted variables description, enriched them using domain knowledge, and computed the distances among them. We used clustering techniques to classify the ...[more]

PMID: 24303228

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Project description:OBJECTIVE: Infobuttons are message-based content search and retrieval functions embedded within other applications that dynamically return information relevant to the clinical task at hand. The objective of this study was to determine whether infobuttons effectively answer providers' questions about medications or affect patient care decisions. DESIGN: The authors implemented and evaluated a medication infobutton application called KnowledgeLink. Health care providers at 18 outpatient clinics were randomized to one of two versions of KnowledgeLink, one that linked to information from Micromedex (Thomson Micromedex, Greenwood Village, Co) and the other to material from SkolarMD (Wolters Kluwer Health, Palo Alto, CA). MEASUREMENTS: Data were collected about the frequency of use and demographics of users, patients, and drugs that were queried. Users were periodically surveyed with short questionnaires and then with a more extensive survey at the end of one year. RESULTS: During the first year, KnowledgeLink was used 7,972 times by 359 users to look up information about 1,723 medications for 4,961 patients. Clinicians used KnowledgeLink twice a month on average, and during an average of 1.2% of patient encounters. KnowledgeLink was used by a wide variety of medical staff, not just physicians and nurse practitioners. The frequency of usage and the questions asked varied with user role (primary care physician, specialist physician, nurse practitioner). Although the median KnowledgeLink session was brief (21 seconds), KnowledgeLink answered users' queries 84% of the time, and altered patient care decisions 15% of the time. Users rated KnowledgeLink favorably on multiple scales, recommended extending KnowledgeLink to other content domains, and suggested enhancing the interface to allow refinement of the query and selection of the target resource. CONCLUSION: An infobutton can satisfy information needs about medications. Although used infrequently and for brief sessions, KnowledgeLink was positively received, answered most users' questions, and had a significant impact on medical decision making. The next steps would be to broaden the domains that KnowledgeLink covers to more specifically tailor results to the user type, to provide options when queries are not immediately answered, and to implement KnowledgeLink within other electronic clinical applications.

Dataset Information

Phenotype Information Retrieval for Existing GWAS Studies.

Publications

Phenotype Information Retrieval for Existing GWAS Studies.

Similar Datasets

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