ArticleBritish journal of clinical pharmacology2018
Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine.
Article in British journal of clinical pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- SV2A expression in blood cells as a possible biomarker candidate for levetiracetam treatment response.Epilepsia · 2026Article
- Prediction of personalized antiseizure medications response based on clinical signatures in epilepsy.Scientific reports · 2025Article
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Developing a machine learning model to assist in predicting treatment success in children with drug-resistant epilepsy.Frontiers in neurology · 2025Article
- Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.Computational and structural biotechnology journal · 2024Review
- Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.Scientific reports · 2024Article
- Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.Epilepsy currents · 2024Review
- Structural connectivity as a predictive factor for responsiveness to levetiracetam treatment in epilepsy.Neuroradiology · 2024Article
- Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning.Scientific reports · 2023Article
- Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review.BMJ open · 2023Article
- Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population.Therapeutic advances in drug safety · 2023Article
- Choosing the Best Antiseizure Medication-Can Artificial Intelligence Help?JAMA neurology · 2022Article
- Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree.Brain sciences · 2022Article
- An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features.Journal of neurology · 2022Article
- Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors.Journal of healthcare engineering · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- Identifying Parkinson's disease with mild cognitive impairment by using combined MR imaging and electroencephalogram.European radiology · 2021Article
- Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.Brain : a journal of neurology · 2021Article
- EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy.Frontiers in medicine · 2021Article
- Neural Tracking of Sound Rhythms Correlates With Diagnosis, Severity, and Prognosis of Disorders of Consciousness.Frontiers in neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes.
methodsRetrospective analysis of PWEs treated with levetiracetam for 3 years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of α, β, θ, δ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes.
resultsCompared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in α band from the F4 channel, β band from Fp2 and F8 channels, θ band from C3 channel (P < 0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of β band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm.
conclusionsThe efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.
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.