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      Defining Phenotypes from Clinical Data to Drive Genomic Research

      1 , 2 , 1 , 1 , 3 , 4 , 1 , 3

      Annual Review of Biomedical Data Science

      Annual Reviews

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          Abstract

          The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks have resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenomes available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. In this review, we highlight the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomic discovery. Use of EHR data has proven a powerful method for elucidating genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.

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          Most cited references 73

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          Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption.

          Recently there has been a remarkable upsurge in activity surrounding the adoption of personal health record (PHR) systems for patients and consumers. The biomedical literature does not yet adequately describe the potential capabilities and utility of PHR systems. In addition, the lack of a proven business case for widespread deployment hinders PHR adoption. In a 2005 working symposium, the American Medical Informatics Association's College of Medical Informatics discussed the issues surrounding personal health record systems and developed recommendations for PHR-promoting activities. Personal health record systems are more than just static repositories for patient data; they combine data, knowledge, and software tools, which help patients to become active participants in their own care. When PHRs are integrated with electronic health record systems, they provide greater benefits than would stand-alone systems for consumers. This paper summarizes the College Symposium discussions on PHR systems and provides definitions, system characteristics, technical architectures, benefits, barriers to adoption, and strategies for increasing adoption.
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            Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

            Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats. To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies. The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University. By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results. Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.
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              Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis.

              The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs.
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                Author and article information

                Journal
                Annual Review of Biomedical Data Science
                Annu. Rev. Biomed. Data Sci.
                Annual Reviews
                2574-3414
                2574-3414
                July 20 2018
                July 20 2018
                : 1
                : 1
                : 69-92
                Affiliations
                [1 ]Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
                [2 ]Department of General Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
                [3 ]Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
                [4 ]Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
                Article
                10.1146/annurev-biodatasci-080917-013335
                © 2018

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