脑卒中多分类预后预测的深度集成优化方法

诊疗前预测急性缺血性脑卒中（AIS）的预后分级，有利于揭示预后转归水平并指导治疗策略，提升方法的预测性能是实现精准医疗的重要指导。利用临床和影像组学的融合特征实施脑卒中的多分类预测，并提出了一种基于融合特征的深度集成优化方法（IABC-DEL）模型，其特征选择方法为Embedded嵌入法和卡方检验，数据不平衡处理方式为Borderline-SMOTE算法，利用Stacking构建深度集成优化模型，基学习器包括深度神经网络（DNN）、长短期记忆网络（LSTM）和门控循环单元（GRU），优化方法为改良人工蜂群算法（IABC）。研究结果表明，深度集成优化方法的预后预测性能优于经典方法和既往研究，Macro-F1 score为87.88%,Macro-AUC为96.27%,ACC为88.02%。因此，基于深度集成优化学习的急性缺血性脑卒中预后模型可对临床诊治和预后康复提供指导意义，并为研究预测提供新的建模思路。

Deep Ensemble Evolutionary Multi-Classification Method for Predicting Prognosis of Stroke

The prognosis grading of acute ischemic stroke（AIS） can effectively assess the prognosis of patients and guide clinical treatment. Improving the method’s prediction performance is an important step toward precision medicine. This paper uses clinical and radiomics fusion features to implement multiclass stroke prediction and proposes a deep integration optimization method. Embedded embedding and the chi-square test are two methods for selecting features. To deal with data imbalance, the Borderline-SMOTE algorithm is used. Stacking is employed in the construction of a deep integration optimization model. Deep neural networks（DNN）, long-short term memory（LSTM）, and gated recurrent unit（GRU） are basic learners. The improved artificial bee colony（IABC） algorithm is used for optimization. The research findings show that the deep integration optimization method outperforms the classical method and advanced research in terms of prognosis prediction performance. Macro-F1 score is 87.88%, Macro-AUC is 96.27%, and ACC is 88.02%. As a result, the acute ischemic stroke prognosis model based on deep integration optimization learning can provide guidance for clinical diagnosis, treatment, and prognosis rehabilitation, as well as new modeling ideas for research and prediction.