{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:39:12Z","timestamp":1776893952748,"version":"3.51.2"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","funder":[{"name":"Public Health Service","award":["T32 GM07347"],"award-info":[{"award-number":["T32 GM07347"]}]},{"name":"National Institute of General Medical Studies"},{"name":"Vanderbilt Medical-Scientist Training Program"},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["R01-LM010685, R21LM011664"],"award-info":[{"award-number":["R01-LM010685, R21LM011664"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"crossref","award":["R01 GM105688"],"award-info":[{"award-number":["R01 GM105688"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Vanderbilt CTSA","award":["1 UL1 RR024975"],"award-info":[{"award-number":["1 UL1 RR024975"]}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Marshfield Clinic","award":["U01HG006389"],"award-info":[{"award-number":["U01HG006389"]}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,1]]},"abstract":"<jats:p>Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites.<\/jats:p><jats:p>Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic.<\/jats:p><jats:p>Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar.<\/jats:p><jats:p>Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.<\/jats:p>","DOI":"10.1093\/jamia\/ocw071","type":"journal-article","created":{"date-parts":[[2016,8,8]],"date-time":"2016-08-08T00:08:11Z","timestamp":1470614891000},"page":"162-171","source":"Crossref","is-referenced-by-count":75,"title":["Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals"],"prefix":"10.1093","volume":"24","author":[{"given":"Pedro L","family":"Teixeira","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Qi","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert M","family":"Cronin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob P","family":"VanHouten","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"},{"name":"Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert J","family":"Carroll","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"LaRose","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 N Oak Ave \u2013 ML8, Marshfield, WI 54449, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa A","family":"Bastarache","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. Trent","family":"Rosenbloom","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"},{"name":"Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Todd L","family":"Edwards","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan M","family":"Roden","sequence":"additional","affiliation":[{"name":"Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA"},{"name":"Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas A","family":"Lasko","sequence":"additional","affiliation":[{"name":"Department of Biomedical 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