Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification. In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.

中文翻译：

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动脉瘤性蛛网膜下腔出血后迟发性脑缺血的预测。基于机器学习的研究

迟发性脑缺血 (DCI) 是动脉瘤性蛛网膜下腔出血 (aSAH) 后的一种严重并发症，可能导致不良预后。机器学习技术在预测 DCI 和改善风险分层方面已显示出前景。在这项研究中，我们旨在开发机器学习模型来预测 aSAH 患者 DCI 的发生。收集患者数据，包括各种临床变量和辅助因素。使用准确度、曲线下面积 (AUC)、准确率、召回率和 F1 分数。数据增强后，随机森林模型表现出最佳性能，AUC 为 0.85。多层感知器神经网络模型的准确度为 0.93，F1 得分为 0.85，使其成为性能最好的模型。阳性临床血管痉挛的存在被认为是预测 DCI 的最重要特征。我们的研究强调了机器学习模型在预测 aSAH 患者 DCI 发生方面的潜力。多层感知器模型表现出优异的性能，表明其在风险分层和临床决策中的实用性。然而，需要对模型进行进一步验证和细化，以确保其在现实环境中的普遍性和适用性。机器学习技术有潜力加强患者护理并改善 aSAH 的预后，但其实施应伴随仔细的评估和验证。