ReviewEpilepsia2019
Machine learning applications in epilepsy.
Review in Epilepsia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 6 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
137 citing papers in PubMed, 6 syntheses or guidelines pooled it, 343 citations in OpenAlex.
- Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps.Epilepsia · 2026Pooled it
- Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study.BMC health services research · 2024Pooled it
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- Pooled it
- Healthcare professionals' knowledge, attitude, and perception of epilepsy surgery: A systematic review.Epilepsy & behavior : E&B · 2021Pooled it
- Epilepsy as a Multiscale Network Disorder: Integrating Precision Therapeutics and Emerging Experimental Platforms.Pharmaceutics · 2026Review
- Unsupervised clustering of electroclinical features in temporal lobe epilepsy: A data-driven approach.Epileptic disorders : international epilepsy journal with videotape · 2026Article
- Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.Encephalitis (Seoul, Korea) · 2026Article
- Deployment of a machine learning-based predictive system for childhood diarrhea in Sub-Saharan Africa.Scientific reports · 2026Article
- Automated video-based differentiation of sleep-related hypermotor epilepsy and parasomnia episodes.NPJ digital medicine · 2026Article
- Development and validation of a machine learning model to predict prognostic outcomes in infantile epileptic spasms syndrome.Frontiers in pediatrics · 2026Article
- Artificial intelligence for automated detection of interictal epileptiform discharges: methodological progress, clinical evaluation, and neurodevelopmental context.Frontiers in cell and developmental biology · 2026Review
- How to report neurotechnology and artificial intelligence studies in epilepsy: Peer-review-inspired recommendations.Epilepsia open · 2025Article
- Personalized biomarkers of multiscale functional alterations in temporal lobe epilepsy.Nature communications · 2025Article
- Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study.Scientific reports · 2025Article
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Language and Memory Network Alterations in Temporal Lobe Epilepsy: A Functional and Structural Connectivity Study.AJNR. American journal of neuroradiology · 2025Article
- Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.Epilepsia · 2025Article
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
77 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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.