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Record W4410051974 · doi:10.1111/epi.18446

Development and validation of International Classification of Diseases, 9th and 10th Revision, Clinical Modification‐based algorithms to identify adult epilepsy in electronic health records

2025· article· en· W4410051974 on OpenAlexaff
Hernan Nicolás Lemus, Jonathan Goldstein, Heng‐Ming Tai, Jung‐Yi Lin, Churl‐Su Kwon, Parul Agarwal, Benjamin Kummer, Mandip S. Dhamoon, Kusum S. Mathews, Sharon Nirenberg, Nathalie Jetté, Leah J. Blank

Bibliographic record

VenueEpilepsia · 2025
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNational Institute on Aging
KeywordsEpilepsyDiagnosis codeMedicineAlgorithmPredictive valueElectronic health recordPediatricsInternal medicineHealth carePsychiatryMathematicsPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: Electronic health records (EHRs) are increasingly used to conduct research and evaluate epilepsy quality of care. We examined the accuracy of International Classification of Diseases, 9th and 10th Revision, Clinical Modification (ICD-9-CM, ICD-10-CM)- and antiseizure medicine (ASM)-based algorithms for adult epilepsy. METHODS: Data from a diverse New York multicenter EHR were queried to identify encounters between January 1, 2012 and September 20, 2018 coded with an epilepsy/seizure ICD-CM code or an ASM. Eight hundred adults were randomly selected (350 with epilepsy-related codes, 150 with an ASM, and 300 with drug-resistant epilepsy codes). With chart review defined as the reference standard, sensitivity (Sn), specificity (Sp), negative predictive value (NPV), positive predictive value (PPV), and Youden index (YI) were calculated to evaluate various ICD-9-CM-, ICD-10-CM-, ± ASM-based algorithms' accuracy in predicting epilepsy. RESULTS: Ninety-four algorithms were tested. A total of 435 (54.4%) patients had definite epilepsy. Estimates ranged as follows: YI = .18-.68, Sn = .59-.95, Sp = .52-.97, PPV = .67-.92, and NPV = .51-.93. The best algorithms were as follows. Highest YI for ICD-9-CM was single encounter with 345 (except 345.2 or 345.3) or 345.2, 345.3, or 780.3 with an ASM (Sn = .95, Sp = .73, PPV = .81, NPV = .92, YI = .68). Highest Y1 for ICD-10-CM was one encounter with G40 in primary diagnostic position or ≥2 encounters with G40 in any diagnostic position (Sn = .82, Sp = .85, PPV = .87, NPV = .80, YI = .67). Highest sensitivity was any encounter with ICD-9-CM 345 or 780.39 or ICD-10-CM G40, G41, or R56.9 (Sn = .96, Sp = .57, PPV = .73, NPV = .93, YI = .53). Highest specificity was ≥1 hospitalization with ICD-9-CM 345.x (except 345.2 and 345.3) or ICD-10-CM G40.x (Sn = .21, Sp = .97, PPV = .89, NPV = .51, YI = .18). SIGNIFICANCE: We identified ICD-9/10-CM-based case definitions (with and without ASM) that were sensitive and specific for epilepsy. Ultimately, extensive algorithms are provided to help inform case definition selection according to future study aims.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.409
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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