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URL: http://phenotype.mc.vanderbilt.edu/
Proper Citation: PheKB (RRID:SCR_005292)
Description: Collaborative environment of building and validating electronic phenotype algorithms using electronic medical records (EMRs) and natural language processing (NLP) for use in genome-wide association studies (GWAS). On this site you can: View existing algorithms, Enter or create new algorithms, Collaborate with others to create or review algorithms, View implementation details for existing algorithms. The Electronic Medical Records and Genomics Network (eMERGE) has investigated whether data captured through routine clinical care using electronic medical records (EMRs) can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format; in addition, natural language processing has also been shown to improve case identification rates. PheKB is an outgrowth of that validation effort. Phenotype algorithms can be viewed by data modalities or methods used: CPT codes, ICD 10 codes, ICD 9 codes, Laboratories, Medications, Vital Signs, Natural Language Processing Algorithms can also be viewed by: * Implementation results (positive predictive value, sensitivity, publications) * Institution * Work Group
Abbreviations: PheKB
Synonyms: Phenotype KnowledgeBase, PheKB - a knowledgebase for discovering phenotypes from electronic medical records
Resource Type: software repository, knowledge environment, software resource
Defining Citation: PMID:20362271
Keywords: phenotype, electronic medical record, medical record, human, clinical, white blood cell, red blood cell, lipid, algorithm, height, cardiac conduction, genome-wide association study, natural language processing
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