ArticleJournal of the American Medical Informatics Association : JAMIA2017
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 95 citations in OpenAlex.
- Late-adolescent weight categories and early kidney disease in young adulthood: a nationwide study of Arab and Jewish Israelis.Pediatric nephrology (Berlin, Germany) · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- Hypertension Prevalence, Diagnosis, and Management for Post-9/11 US Veterans.Journal of the American Heart Association · 2026Article
- Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.Scientific reports · 2026Article
- Application of an Externally Developed Algorithm to Identify Research Cases and Controls from EHR Data: Trials and Triumphs.Applied clinical informatics · 2025Article
- Accounting for racial bias and social determinants of health in a model of hypertension control.BMC medical informatics and decision making · 2025Article
- Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.JAMIA open · 2024Article
- Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks.BMC cardiovascular disorders · 2024Article
- The Association of Weight Categories in Adolescence with Cardiovascular Morbidity in Young Adult Israeli Arabs-A Nationwide Study.Journal of clinical medicine · 2024Article
- Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women.Scientific reports · 2024Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- State-Level Hypertension Prevalence and Control Among Adults in the U.S.American journal of preventive medicine · 2024Article
- Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.Frontiers in immunology · 2024Article
- Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States.American journal of hypertension · 2023Article
- Development of a Hypertension Electronic Phenotype for Chronic Disease Surveillance in Electronic Health Records: Key Analytic Decisions and Their Effects.Preventing chronic disease · 2023Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia.BMC nephrology · 2022Article
- What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.Human genetics · 2022Review
- Phenotypic Presentations of Heart Failure Among Patients With Chronic Inflammatory Diseases.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
objectivePhenotyping 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. MATERIALS AND
methodsWe 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.
resultsRandom 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.
conclusionThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.