ArticleEpilepsia2019
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Article in Epilepsia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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
20 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Quantifying the impact of a computer-aided diagnostic score on the clinical diagnosis of functional seizures.Epilepsia · 2026Article
- Supervised machine learning compared to large language models for identifying functional seizures from medical records.Epilepsia · 2025Article
- Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.Health science reports · 2025Article
- Disparities in seizure outcomes revealed by large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.Epilepsia open · 2024Article
- Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.Epilepsy currents · 2024Review
- Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.Neurology · 2024Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Towards a practical use of text mining approaches in electrodiagnostic data.Scientific reports · 2023Article
- Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.JMIR medical informatics · 2022Article
- Article
- Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review.JMIR medical informatics · 2022Article
- Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.Acta neurochirurgica. Supplement · 2022Review
- Early identification of epilepsy surgery candidates: A multicenter, machine learning study.Acta neurologica Scandinavica · 2021Article
- Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.Epilepsia · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients). The model was tested on 8340 notes from 3776 patients with epilepsy whose surgical candidacy status was unknown (2029 male, 1747 female, median age = 9 years; age range = 0-60 years). Multiple linear regression using demographic variables as covariates was used to test for correlations between patient race and surgical candidacy scores. After accounting for other demographic and socioeconomic variables, patient race, gender, and primary language did not influence surgical candidacy scores (P > .35 for all). Higher scores were given to patients >18 years old who traveled farther to receive care, and those who had a higher family income and public insurance (P < .001, .001, .001, and .01, respectively). Demographic effects on surgical candidacy scores appeared to reflect patterns in patient referrals.
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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.