摘要癫痫是由脑部神经元高度同步化异常放电引起的发作性、短暂性、刻板性的脑功能失调，发作时机难以预测，目前主要治疗方式为抗癫痫药物与手术治疗。其诊断和治疗需要大量的辅助手段与临床经验。在癫痫的发作预测、药物治疗预后、手术治疗评估等多个临床方面，机器学习可以通过对数据的深层次挖掘、纳入多个临床和影像因素、建立对应的学习模型，提高癫痫的诊断效率与准确性，实现抗癫痫药物的个体化应用，改善癫痫患者的术前评估与预后情况。

abstractsEpilepsy is an episodic, transient, stereotypic brain dysfunction caused by highly synchronized abnormal neuronal discharges in the brain, with unpredictable timing of seizures, for which the main treatment modalities are antiepileptic drugs and surgery. Its diagnosis and treatment require a large number of aids and clinical experience. For multiple clinical aspects of epilepsy, such as seizure prediction, drug therapy prognosis, and surgical treatment evaluation, machine learning can incorporate multiple clinical and imaging factors through deep mining of data, establish corresponding learning models, improve the efficiency and accuracy of epilepsy diagnosis, realize individualized application of antiepileptic drugs, and improve the preoperative evaluation and prognosis of epilepsy patients.