Trial reportEpilepsia2023
Identification of patients with epilepsy using automated electronic health records phenotyping.
Trial report in Epilepsia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.Journal of medical Internet research · 2026Article
- Comparing three natural language processing methods for the automatic identification of epilepsy patients from French clinical notes.Epilepsia · 2026Article
- Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke.Journal of the American Heart Association · 2025Article
- Evaluating health-related quality of life and health resource utilization among Gabapentinoid users with substance use disorder in the US: Trends, co-prescriptions, and cost analysis.Exploratory research in clinical and social pharmacy · 2025Article
- A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Using electronic medical records to analyze outpatient visits of persons with epilepsy during the pandemic-experience from a low middle income country.Acta epileptologica · 2025Article
- Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.Epilepsy research · 2024Article
- Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.JMIR medical informatics · 2024Article
- Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records.Brain communications · 2024Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveUnstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy.
methodsThe ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping.
resultsOur study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE: A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.
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