[{"innerBlocks":[],"name":"core\/freeform","postId":1225,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"A Nashville, Tenn.-based healthcare provider is leveraging informatics, including natural language processing, to find linkages between de-identified genotype data and electronic health record data.

Launched in late 2016, Vanderbilt University Medical Center\u2019s Phenotyping and PheWAS Core Services are available to researchers from VUMC and other institutions.

The services, offered under VUMC\u2019s Center for Precision Medicine, include clinical phenotyping\u2014deriving phenotypes from EHR data\u2014to uncover clinical knowledge, gene-disease relationships (genomics, GWAS, and PheWAS), as well as gene-drug-outcome relationships (pharmacogenomics).

According to VUMC, the mission is to apply advanced informatics methods to \u201cunderstand\u201d unstructured\u2014and sometimes inaccurate\u2014biomedical text and electronic medical record data.

\u201cEHR data can be messy, but it contains perhaps the single richest source of disease history, drug exposures and their response, and prognosis available for research,\u201d says Josh Denny, MD, director of the Center for Precision Medicine and vice president for personalized medicine.

\u201cUsing advanced informatics through our core, we can help researchers go beyond what might be gleaned from demographics, billing codes and other structured data, to draw finer distinctions within the patient population based on unlabeled data, such as information in clinical notes,\u201d adds Denny.

VUMC-CROP.jpg<\/bsp-image>Also See<\/b>: EHR data mining identifies undiagnosed genetic diseases<\/a>

PheWAS, which stands for phenome-wide association study, consists of taking a genetic variant of interest and using custom algorithms to scan for associations with International Classification of Diseases codes appearing in the EHR.

\u201cThe most common PheWAS approach is using billing codes,\u201d observes Denny. \u201cThey\u2019re imperfect, but they\u2019re still very valuable.\u201d

VUMC\u2019s vast clinical repositories include medical records for about 2.8 million patients as well as DNA specimens from a subset of its EHR population\u2014approximately 240,000 patients\u2014with more than 90,000 of these specimens having been genotyped.

\u201cTheir ability to work with investigators to move from what may at first appear to be a simple clinical question, through the complexities of identifying a research cohort and defining the clinical outcome, is tremendously valuable,\u201d said Sara Van Driest, MD, assistant professor of pediatrics and medicine.

\u201cThey have specific expertise in helping to translate clinical questions into electronic algorithms that make it possible to study large cohorts of patients,\u201d added Van Driest.","saveContent":"A Nashville, Tenn.-based healthcare provider is leveraging informatics, including natural language processing, to find linkages between de-identified genotype data and electronic health record data.

Launched in late 2016, Vanderbilt University Medical Center\u2019s Phenotyping and PheWAS Core Services are available to researchers from VUMC and other institutions.

The services, offered under VUMC\u2019s Center for Precision Medicine, include clinical phenotyping\u2014deriving phenotypes from EHR data\u2014to uncover clinical knowledge, gene-disease relationships (genomics, GWAS, and PheWAS), as well as gene-drug-outcome relationships (pharmacogenomics).

According to VUMC, the mission is to apply advanced informatics methods to \u201cunderstand\u201d unstructured\u2014and sometimes inaccurate\u2014biomedical text and electronic medical record data.

\u201cEHR data can be messy, but it contains perhaps the single richest source of disease history, drug exposures and their response, and prognosis available for research,\u201d says Josh Denny, MD, director of the Center for Precision Medicine and vice president for personalized medicine.

\u201cUsing advanced informatics through our core, we can help researchers go beyond what might be gleaned from demographics, billing codes and other structured data, to draw finer distinctions within the patient population based on unlabeled data, such as information in clinical notes,\u201d adds Denny.

VUMC-CROP.jpg<\/bsp-image>Also See<\/b>: EHR data mining identifies undiagnosed genetic diseases<\/a>

PheWAS, which stands for phenome-wide association study, consists of taking a genetic variant of interest and using custom algorithms to scan for associations with International Classification of Diseases codes appearing in the EHR.

\u201cThe most common PheWAS approach is using billing codes,\u201d observes Denny. \u201cThey\u2019re imperfect, but they\u2019re still very valuable.\u201d

VUMC\u2019s vast clinical repositories include medical records for about 2.8 million patients as well as DNA specimens from a subset of its EHR population\u2014approximately 240,000 patients\u2014with more than 90,000 of these specimens having been genotyped.

\u201cTheir ability to work with investigators to move from what may at first appear to be a simple clinical question, through the complexities of identifying a research cohort and defining the clinical outcome, is tremendously valuable,\u201d said Sara Van Driest, MD, assistant professor of pediatrics and medicine.

\u201cThey have specific expertise in helping to translate clinical questions into electronic algorithms that make it possible to study large cohorts of patients,\u201d added Van Driest.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Vanderbilt helps researchers find linkages between genotype, EHR data

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