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2022, Epilepsia. DOI: 10.1111/epi.17320
Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery
Omar Yossofzai , Aria Fallah , Cassia Maniquis , Shelly Wang , John Ragheb , Alexander G. Weil , Tristan Brunette‐Clement , Andrea Andrade , George M. Ibrahim , Nicholas Mitsakakis , Elysa Widjaja
Abstract:
Objective
There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery.

Methods
This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach.

Results
Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69–.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66–.82; sensitivity = .87, 95% CI = .81–.94; specificity = .58, 95% CI = .47–.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63–.80; sensitivity = .72, 95% CI = .63–.82; specificity = .66, 95% CI = .53–.77) in the testing cohort (p = .005).

Significance
This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions.
2022-07-10 09:29:00
#paper doi:10.1111/epi.17320 Epilepsia, 2022. Development and validation of machine learning models for prediction of seizure outcome after pediatric epilepsy surgery. 小儿癫痫手术后报告的癫痫发作结果存在很大差异,并且缺乏可以评估术后癫痫发作自由概率的个体化预测工具。本研究的目的是开发和验证用于预测小儿癫痫手术后无癫痫发作的监督机器学习 (ML) 模型。这是一项针对在北美五个儿科癫痫中心接受癫痫手术的儿童的多中心回顾性研究。收集临床信息、诊断调查和手术特征,并将其用作预测术后 1 年无癫痫发作结果的特征。数据集被随机分成 80% 的训练数据和 20% 的测试数据。使用 10 倍交叉验证模型开发,在训练队列上评估了 5 个特征集和 7 个 ML 分类器的 35 个组合。在测试队列中评估 ML 分类器和特征集的最佳组合的性能,并与经典统计方法逻辑回归进行比较。在纳入的 801 名患者中,61.3% 的患者术后 1 年无癫痫发作。在模型开发过程中,最佳组合是 XGBoost ML 算法,它具有来自单变量特征集的五个特征,包括抗癫痫药物数量、磁共振成像病变、癫痫发作年龄、视频脑电图一致性和手术类型,平均面积低于0.73 的曲线 (AUC)(95% 置信区间 [CI] = .69–.77)。然后在测试队列上评估 XGBoost 和单变量特征集的组合并达到 0.74 的 AUC(95% CI = .66–.82;敏感性 = .87,95% CI = .81–.94;特异性 = .58, 95% CI = .47–.71)。XGBoost 模型优于逻辑回归模型(AUC = .72, 95% CI = .63–.80;敏感性 = .72, 95% CI = .63–.82;特异性 = .66, 95% CI = .53 –.77) 在测试队列 (p  = .005)。本研究确定了重要特征并验证了用于预测小儿癫痫手术后无癫痫发作概率的 ML 算法 XGBoost。改善癫痫手术的预后对于术前咨询至关重要,并将为治疗决策提供信息。
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