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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedPolubriaginof FCG, Vanguri R, Quinnies K
Disease heritability inferred from familial relationships reported in medical records.
Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. This study used EHR data to compute heritability estimates for 500 disease phenotypes. These analyses provided a validation of the use of EHRs for genetics and disease research.
AHRQ-funded; HS021816; HS022961.
Citation: Polubriaginof FCG, Vanguri R, Quinnies K .
Disease heritability inferred from familial relationships reported in medical records.
Cell 2018 Jun 14;173(7):1692-704.e11. doi: 10.1016/j.cell.2018.04.032..
Keywords: Data, Family Health and History, Genetics, Health Information Technology (HIT), Electronic Health Records (EHRs)
Richesson RL, Sun J, Pathak J
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
The authors sought to use electronic health records data to advance understanding of disease risk and drug response, and to support the practice of precision medicine on a national scale. They found that machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, as it comes from data rather than experts. They suggested that research networks and phenotype developers cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and modernize biomedical research.
AHRQ-funded; HS023921; HS023077.
Citation: Richesson RL, Sun J, Pathak J .
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
Artif Intell Med 2016 Jul;71:57-61. doi: 10.1016/j.artmed.2016.05.005.
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Keywords: Data, Electronic Health Records (EHRs), Genetics, Patient-Centered Healthcare