ArticleEpilepsia2020
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Article in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
30 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.Epilepsia · 2023Trial
- Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models.Annals of clinical and translational neurology · 2026Article
- Review
- Areas of research priorities in epilepsy: A position paper of the European Reference Network for Rare and Complex Epilepsies, EpiCARE.Epilepsia open · 2026Article
- AI language model applications for early diagnosis of childhood epilepsy based on unstructured first-visit patient narratives: A cohort study.Epileptic disorders : international epilepsy journal with videotape · 2025Article
- Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing.Journal of neurological surgery. Part B, Skull base · 2025Article
- Supervised machine learning compared to large language models for identifying functional seizures from medical records.Epilepsia · 2025Article
- Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials.Health care science · 2024Article
- Testing an online screening tool for epilepsy surgery evaluation.Arquivos de neuro-psiquiatria · 2024Article
- Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.bioRxiv : the preprint server for biology · 2024Article
- Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.Epilepsia open · 2024Article
- Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.Neurology · 2024Article
- NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy.Scientific reports · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveDelay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores.
methodsThe application was trained on notes from (1) patients with a diagnosis of epilepsy and a history of resective epilepsy surgery and (2) patients who were seizure-free without surgery. The testing set included all patients with unknown surgical candidacy status and an upcoming neurology visit. Training and testing sets were updated weekly for 1 year. One- to three-word phrases contained in patients' notes were used as features. Patients prospectively identified by the application as candidates for surgery were manually reviewed by two epileptologists. Performance metrics were defined by comparing NLP-derived surgical candidacy scores with surgical candidacy status from expert chart review.
resultsThe training set was updated weekly and included notes from a mean of 519 ± 67 patients. The area under the receiver operating characteristic curve (AUC) from 10-fold cross-validation was 0.90 ± 0.04 (range = 0.83-0.96) and improved by 0.002 per week (P < .001) as new patients were added to the training set. Of the 6395 patients who visited the neurology clinic, 4211 (67%) were evaluated by the model. The prospective AUC on this test set was 0.79 (95% confidence interval [CI] = 0.62-0.96). Using the optimal surgical candidacy score threshold, sensitivity was 0.80 (95% CI = 0.29-0.99), specificity was 0.77 (95% CI = 0.64-0.88), positive predictive value was 0.25 (95% CI = 0.07-0.52), and negative predictive value was 0.98 (95% CI = 0.87-1.00). The number needed to screen was 5.6. SIGNIFICANCE: An electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.
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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.