ArticleEpilepsia2026
Comparing three natural language processing methods for the automatic identification of epilepsy patients from French clinical notes.
Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Structured EEG report complements the prognostic stratification of status epilepticus.Journal of neurology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveManual review of clinical notes by experts remains the reference standard for identifying patients with epilepsy in health databases. However, this process is labor-intensive and time-consuming due to the unstructured nature of text. Prior studies have shown the potential of natural language processing for automated phenotyping. We aim to develop and validate algorithms capable of identifying patients with epilepsy based on a set of clinical notes.
methodsA population of 109 448 patients was selected from the Assistance Publique-Hôpitaux de Paris (AP-HP) Clinical Data Warehouse (CDW) (38 hospitals in Paris, France) based on the presence of an International Classification of Diseases, Tenth Revision (ICD-10) diagnostic code related to epilepsy (G40/G41) or mimicking disorders (R53/R55/R56), or the mention of at least one antiseizure medication in their medical chart. From this pre-screened population, 6733 sentences (from 2700 patients) were labeled as indicative or not indicative of epilepsy, and 3000 patients were selected randomly for manual review by a neurologist. We compared a "basic" keyword-based method, a rule-based method, and a pretrained language model for identifying epilepsy-related sentences and classifying patients with epilepsy. We reported the F1 score of each method.
resultsAt the sentence level, the pretrained language model reached the highest F1 score of .95 (95% confidence interval [CI]: .95-.96) outperforming the rule-based method .87 (95% CI: .86-.88) and the basic method .81 (95% CI: .80-.81). At the patient level, the pretrained language model also achieved the best F1 score .95 (95% CI: .94-.96) compared to the rule-based method .93 (95% CI: .91-.94) and the basic method .82 (95% CI: .81-.84). SIGNIFICANCE: Both the rule-based and the pretrained language models achieved high performance. These algorithms can automatically identify patients with epilepsy from unstructured clinical notes in French data warehouses, supporting large-scale phenotyping and the detection of epilepsy as a comorbidity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.