摘要
为了准确预测病人肺部手术后并发症的发生,提出了一种融合神经记忆常微分方程(neural memory ordinary differential equation,nmODE)的并发症预测模型。首先,利用极限梯度提升(extreme gradient boosting,XGBoost)树结构对数据进行编码,并提取其特征重要性。然后,使用长短时记忆神经网络对数据的相关特征依赖性进行分析,并提取处理后的特征。最后,利用nmODE的记忆和学习能力,对提取的特征进行深入分析,并得出最终的预测结果。通过实验评估,在肺部术后并发症数据集中,证明了提出模型的效果优于现有模型,同时可以为预测肺部手术后并发症的发生提供更准确的结果。
In order to accurately predict the occurrence of postoperative complications in patients'lungs,a complication prediction model combining neural memory ordinary differential equation(nmODE)is proposed.The method of this model is as follows:firstly,an extreme gradient boosting(XGBoost)tree structure is used to encode the data and extract its feature importance.Then,a long short-term memory neural network is employed to analyze the dependency of the data's relevant features and extract the processed features.Finally,by utilizing the memory and learning capabilities of nmODE,the extracted features are deeply analyzed to obtain the final prediction results.Experimental evaluation has demonstrated the effectiveness of the proposed model in the dataset of postoperative complications in the lungs,showing superior performance compared with existing models.Furthermore,it can provide more accurate results for predicting the occurrence of postoperative complications in lung surgery.
作者
熊立鹏
徐修远
牛颢
陈楠
章毅
XIONG Lipeng;XU Xiuyuan;NIU Hao;CHEN Nan;ZHANG Yi(College of Computer Science,Sichuan University,Chengdu 610065,China;West China Hospital,Sichuan University,Chengdu 610065,China)
出处
《智能系统学报》
北大核心
2025年第1期198-205,共8页
CAAI Transactions on Intelligent Systems
关键词
疾病预测
异构表格数据
神经记忆常微分方程
极限梯度提升
长短时记忆神经网络
合成少数过采样技术
类别不平衡
病人预后
disease prediction
heterogeneous tabular data
neural memory ordinary differential equation
extreme gradient boosting
long short-term memory
synthetic minority oversampling technique
class imbalance
patient prognosis
