ArticleEpilepsia2023
Creating rare epilepsy cohorts using keyword search in electronic health records.
Article in Epilepsia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.JMIR medical informatics · 2024Article
- Article
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
objectiveAdministrative codes to identify people with rare epilepsies in electronic health records are limited. The current study evaluated the use of keyword search as an alternative method for rare epilepsy cohort creation using electronic health records data.
methodsData included clinical notes from encounters with International Classification of Diseases, Ninth Revision (ICD-9) codes for seizures, epilepsy, and/or convulsions during 2010-2014, across six health care systems in New York City. We identified cases with rare epilepsies by searching clinical notes for keywords associated with 33 rare epilepsies. We validated cases via manual chart review. We compared the performance of keyword search to manual chart review using positive predictive value (PPV), sensitivity, and F-score. We selected an initial combination of keywords using the highest F-scores.
resultsData included clinical notes from 77 924 cases with ICD-9 codes for seizures, epilepsy, and/or convulsions. The all-keyword search method identified 6095 candidates, and manual chart review confirmed that 2068 (34%) had a rare epilepsy. The initial combination method identified 1862 cases with a rare epilepsy, and this method performed as follows: PPV median = .64 (interquartile range [IQR] = .50-.81, range = .20-1.00), sensitivity median = .93 (IQR = .76-1.00, range = .10-1.00), and F-score median = .71 (IQR = .63-.85, range = .18-1.00). Using this method, we identified four cohorts of rare epilepsies with over 100 individuals, including infantile spasms, Lennox-Gastaut syndrome, Rett syndrome, and tuberous sclerosis complex. We identified over 50 individuals with two rare epilepsies that do not have specific ICD-10 codes for cohort creation (epilepsy with myoclonic atonic seizures, Sturge-Weber syndrome). SIGNIFICANCE: Keyword search is an effective method for cohort creation. These findings can improve identification and surveillance of individuals with rare epilepsies and promote their referral to specialty clinics, clinical research, and support groups.
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.