Lijun Yao1, Mengting Cai2, Yang Chen3, Chunhong Shen2, Lei Shi4, Yi Guo5. 1. Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai 200124, PR China. Electronic address: lyao@tongji.edu.cn. 2. Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China. 3. School of Computer Science, Fudan University, Shanghai 201203, PR China. 4. The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100080, PR China. 5. Department of Neurology, Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, PR China. Electronic address: yiguo@zju.edu.cn.
Abstract
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. METHODS: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. RESULTS: Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. SIGNIFICANCES: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.
OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. METHODS: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. RESULTS: Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. SIGNIFICANCES: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.
Authors: Nicolas Schneider; Keywan Sohrabi; Henning Schneider; Klaus-Peter Zimmer; Patrick Fischer; Jan de Laffolie Journal: Front Med (Lausanne) Date: 2021-05-24
Authors: Johann de Jong; Ioana Cutcutache; Matthew Page; Sami Elmoufti; Cynthia Dilley; Holger Fröhlich; Martin Armstrong Journal: Brain Date: 2021-07-28 Impact factor: 13.501