title: Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Pedro L
authors: PL
authors: Teixeira
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Wei-Qi
authors: WQ
authors: Wei
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Robert M
authors: RM
authors: Cronin
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Huan
authors: H
authors: Mo
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.|Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Jacob P
authors: JP
authors: VanHouten
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Robert J
authors: RJ
authors: Carroll
authors: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 N Oak Ave - ML8, Marshfield, WI 54449, USA.
authors: Eric
authors: E
authors: LaRose
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Lisa A
authors: LA
authors: Bastarache
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.|Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: S Trent
authors: ST
authors: Rosenbloom
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Todd L
authors: TL
authors: Edwards
authors: Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.|Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Dan M
authors: DM
authors: Roden
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Thomas A
authors: TA
authors: Lasko
authors: Center for Human Genetics, Marshfield Clinic Research Foundation, 1000 N Oak Ave-MLR, Marshfield, WI 54449, USA.
authors: Richard A
authors: RA
authors: Dart
authors: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 N Oak Ave - ML8, Marshfield, WI 54449, USA.
authors: Anne M
authors: AM
authors: Nikolai
authors: Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 N Oak Ave - ML8, Marshfield, WI 54449, USA.
authors: Peggy L
authors: PL
authors: Peissig
authors: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA josh.denny@vanderbilt.edu.|Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
authors: Joshua C
authors: JC
authors: Denny
mesh_terms: D000368:Aged; D000465:Algorithms; D001795:Blood Pressure Determination; D059019:Clinical Coding; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D016247:Information Storage and Retrieval / Q000379:methods; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D010641:Phenotype; D012372:ROC Curve
keywords: electronic health records; hypertension; machine learning; natural language processing; phenotyping algorithms; random forests