Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is uncertain. With the rapid advancement of computer technology, machine learning (ML) offers new opportunities for creating more accurate prediction models. However, the potential of ML in predicting PSE is still not well understood. The purpose of this study was to develop prediction models for PSE among ischemic stroke patients. Patients with ischemic stroke from two stroke centers were included in this retrospective cohort study. At the baseline level, 33 input variables were considered candidate features. The 2-year PSE prediction models in the derivation cohort were built using six ML algorithms. The predictive performance of these machine learning models required further appraisal and comparison with the reference model using the conventional triage classification information. The Shapley additive explanation (SHAP), based on fair profit allocation among many stakeholders according to their contributions, is used to interpret the predicted outcomes of the naive Bayes (NB) model. A total of 1977 patients were included to build the predictive model for PSE. The Boruta method identified NIHSS score, hospital length of stay, D-dimer level, and cortical involvement as the optimal features, with the receiver operating characteristic curves ranging from 0.709 to 0.849. An additional 870 patients were used to validate the ML and reference models. The NB model achieved the best performance among the PSE prediction models with an area under the receiver operating curve of 0.757. At the 20 % absolute risk threshold, the NB model also provided a sensitivity of 0.739 and a specificity of 0.720. The reference model had poor sensitivities of only 0.15 despite achieving a helpful AUC of 0.732. Furthermore, the SHAP method analysis demonstrated that a higher NIHSS score, longer hospital length of stay, higher D-dimer level, and cortical involvement were positive predictors of epilepsy after ischemic stroke. Our study confirmed the feasibility of applying the ML method to use easy-to-obtain variables for accurate prediction of PSE and provided improved strategies and effective resource allocation for high-risk patients. In addition, the SHAP method could improve model transparency and make it easier for clinicians to grasp the prediction model's reliability.

中文翻译：

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用于预测中风后癫痫的可解释机器学习模型的开发和验证


癫痫是缺血性中风后的严重并发症。尽管有两项研究开发了脑卒中后癫痫（PSE）的预测模型，但其准确性仍然不足，并且其对不同人群的适用性尚不确定。随着计算机技术的快速进步，机器学习（ML）为创建更准确的预测模型提供了新的机会。然而，机器学习在预测 PSE 方面的潜力仍未得到充分了解。本研究的目的是开发缺血性中风患者 PSE 的预测模型。来自两个卒中中心的缺血性卒中患者被纳入这项回顾性队列研究。在基线水平，33 个输入变量被视为候选特征。推导队列中的 2 年 PSE 预测模型是使用六种 ML 算法构建的。这些机器学习模型的预测性能需要进一步评估，并与使用传统分类分类信息的参考模型进行比较。沙普利附加解释（SHAP）基于许多利益相关者根据其贡献进行公平利润分配，用于解释朴素贝叶斯（NB）模型的预测结果。总共纳入了 1977 名患者来构建 PSE 预测模型。 Boruta 方法将 NIHSS 评分、住院时间、D-二聚体水平和皮质受累确定为最佳特征，受试者工作特征曲线范围为 0.709 至 0.849。另外 870 名患者被用来验证 ML 和参考模型。 NB 模型在 PSE 预测模型中取得了最好的性能，接收者操作曲线下面积为 0.757。 在 20% 绝对风险阈值下，NB 模型还提供了 0.739 的敏感性和 0.720 的特异性。尽管 AUC 达到了 0.732，但参考模型的灵敏度很差，仅为 0.15。此外，SHAP方法分析表明，较高的NIHSS评分、较长的住院时间、较高的D-二聚体水平和皮质受累是缺血性卒中后癫痫的积极预测因素。我们的研究证实了应用ML方法利用易于获取的变量准确预测PSE的可行性，并为高危患者提供改进的策略和有效的资源分配。此外，SHAP方法可以提高模型透明度，使临床医生更容易掌握预测模型的可靠性。