ArticleCMAJ open
Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.
Article in CMAJ open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.Scientific reports · 2026Article
- OpenNotes in dentistry as a strategy for patient engagement: opportunities, barriers, and a framework for implementation.Frontiers in oral health · 2026Review
- Article
- Hypertension in Pregnancy: Current Challenges and Future Opportunities for Surveillance and Research.Journal of women's health (2002) · 2024Article
- Prompt engineering for digital mental health: a short review.Frontiers in digital health · 2024Review
- Exploring the reliability of inpatient EMR algorithms for diabetes identification.BMJ health & care informatics · 2023Article
- Filling gaps in female gout: a cross-sectional study of comorbidities in 192 037 hospitalised patients.RMD open · 2023Observational
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundCase identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning.
methodsA cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan. 1 and June 30, 2015, were randomly selected, and we compared the performance of EMR phenotype algorithms developed using machine learning with an algorithm based on the Canadian version of the
resultsOf our study sample (
interpretationWe found that hypertension tends to have clear documentation in EMRs and is well classified by concept search on free text. Machine learning can provide insights into how and where conditions are documented in EMRs and suggest nonmachine-learning phenotypes to implement.
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Registered trials
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