ArticleObesity science & practice2024
Automated extraction of weight, height, and obesity in electronic medical records are highly valid.
Article in Obesity science & practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity.Nutrients · 2026Article
- Same ICD-10 code, different estimands: four Polish National Health Fund obesity series, 2015-2025.Frontiers in public health · 2026Article
- Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automated extraction of weight, height, and obesity in electronic medical records are highly valid.Obesity science & practice · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule-based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results: The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions: This study developed a highly valid rule-based EMR algorithm that detects height and weight. This could allow large-scale analyses using obesity that were previously not possible.
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