We are analyzing https://link.springer.com/article/10.1186/s13073-015-0166-y.

Title:
Extracting research-quality phenotypes from electronic health records to support precision medicine | Genome Medicine
Description:
The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
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Keywords {🔍}

pubmed, google, scholar, article, data, electronic, clinical, genetic, central, cas, research, ehr, health, medical, med, study, information, ehrs, studies, record, phenotypes, records, diseases, association, genet, phenotyping, biobank, inform, denny, care, disease, biobanks, phenotype, dna, patient, assoc, discovery, challenges, risk, variants, network, algorithm, algorithms, diabetes, emerge, medicine, clin, genomic, systems, associations,

Topics {✒️}

membrane-transporter-encoding gene abcb1 gov/research/umls/snomed/snomed_main age-related macular degeneration eosinophilic esophagitis [103] eosinophilic esophagitis clinician-driven automated system high-throughput genetic research genome-wide association studies genome-wide association study discover gene-disease associations cross-terminology mapping challenges extracting research-quality phenotypes phenome-wide association studies rapid search algorithms high-throughput clinical phenotyping drug-induced liver injury warfarin-related bleeding events phenome-wide association study nih-funded national center steady-state warfarin dose cross-sectional survey conducted human genome project article download pdf electronic health record pertinent keywords european-origin pediatric cohorts population-based dna biobank gov/quality-data-model ehr-based genetic research emr-linked pediatric cohorts large practice-based dataset article wei support precision medicine snomed-ct includes links electronic medical record ehr-based genetic studies ehr-based phenotyping studies adverse drug-drug interaction advanced ehr-based phenotyping multi-ethnic cohort derived ehr-based phenotype definitions geisinger health system enable personalized medicine ehr-derived genetic research newton km o'toole mf electronic health records include drug-response phenotypes ehr-linked genetic data explore ehr data

Schema {🗺️}

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         headline:Extracting research-quality phenotypes from electronic health records to support precision medicine
         description:The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
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      headline:Extracting research-quality phenotypes from electronic health records to support precision medicine
      description:The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
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         Electronic Health Record
         Eosinophilic Esophagitis
         Unify Medical Language System
         National Human Genome Research Institute
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         Metabolomics
         Bioinformatics
         Medicine/Public Health
         general
         Cancer Research
         Systems Biology
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