Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Epilepsia Open. 2023 Dec;8(4):1362-1368. doi: 10.1002/epi4.12800. Epub 2023 Aug 22.
The purpose of the current endeavor was to evaluate the feasibility of using easily accessible and applicable clinical information (based on history taking and physical examination) in order to make a reliable differentiation between idiopathic generalized epilepsy (IGE) versus focal epilepsy using machine learning (ML) methods.
The first phase of the study was a retrospective study of a prospectively developed and maintained database. All patients with an electro-clinical diagnosis of IGE or focal epilepsy, at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022, were included. The first author selected a set of clinical features. Using the stratified random portioning method, the dataset was divided into the train (70%) and test (30%) subsets. Different types of classifiers were assessed and the final classification was made based on their best results using the stacking method.
A total number of 1445 patients were studied; 964 with focal epilepsy and 481 with IGE. The stacking classifier led to better results than the base classifiers in general. This algorithm has the following characteristics: precision: 0.81, sensitivity: 0.81, and specificity: 0.77.
We developed a pragmatic algorithm aimed at facilitating epilepsy classification for individuals whose epilepsy begins at age 10 years and older. Also, in order to enable and facilitate future external validation studies by other peers and professionals, the developed and trained ML model was implemented and published via an online web-based application that is freely available at http://www.epiclass.ir/f-ige.
本研究旨在评估利用易于获取和应用的临床信息（基于病史采集和体格检查）通过机器学习（ML）方法对特发性全面性癫痫（IGE）与局灶性癫痫进行可靠区分的可行性。
研究的第一阶段是一项回顾性研究，基于前瞻性开发和维护的数据库。纳入 2008 年至 2022 年在伊朗设拉子医科大学门诊癫痫诊所就诊的经电临床诊断为 IGE 或局灶性癫痫的所有患者。第一作者选择了一组临床特征。使用分层随机划分方法，将数据集分为训练（70%）和测试（30%）子集。评估了不同类型的分类器，并使用堆叠方法基于其最佳结果进行最终分类。
共研究了 1445 例患者，其中 964 例为局灶性癫痫，481 例为 IGE。堆叠分类器总体上优于基础分类器。该算法具有以下特点：精度为 0.81，灵敏度为 0.81，特异性为 0.77。
我们开发了一种实用的算法，旨在为 10 岁及以上起病的癫痫患者进行癫痫分类提供便利。此外，为了能够并促进其他同行和专业人员未来进行外部验证研究，我们通过在线网络应用程序实施并发布了开发和训练的 ML 模型，该应用程序可在 http://www.epiclass.ir/f-ige 上免费获得。
