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Table 2 Machine learning algorithms for the response to AEDs treatment

From: Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

Study Study design Prediction target Training and testing cohorts Algorithms Predictive features Performance External validation
Devinsky et al. 2017 [18] Retrospective Choice of AEDs for individual patients 40, 000 patients for training and 10, 000 patients for testing Machine learning algorithm About 5 000 features AUC of 0.72 Yes
An et al. 2017 [19] Retrospective Prediction of DRE 175, 735 were training cohort and the other 117, 157 were test cohort Multivariate logistic regression, support vector machine and random forest 1 270 features AUC of 0.76 No
Petrovski et al. 2009 [20] Prospective Prediction of AEDs treatment outcomes 115 patients with newly diagnosed epilepsy K-nearest neighbors 279 candidate genes Accuracy of 83.5% and sensitivity above 80% Yes
Yao et al. 2019 [22] Retrospective Prediction of AEDs treatment outcomes 287 patients with newly diagnosed epilepsy Decision tree, random forest, support vector machine, XGBoost and logistic regression Demographic features, medical history, EEG and MRI F1 score and AUC value showed good performance No
Zhang et al. 2018 [23] Retrospective Prediction of efficacy of levetiracetam 46 patients with newly diagnosed epilepsy Support vector machine Clinical features and sample entropy 75.0% accuracy in the training set and 90% in the test set No
  1. AEDs Antiepileptic drugs, DRE Drug resistant epilepsy, AUC Area under the curve, EEG Electroencephalogram, MRI Magnetic resonance imaging