基于独立成分分析与LSTM网络的患者病情预测模型研究

Research on patient condition prediction model based on independent component analysis and LSTM network

摘要

为提高脑疾病患者病情预测的精度,以阿尔茨海默脑疾病为研究对象,提出一种基于独立成分分析法与LSTM的预测模型。模型分为3个阶段,首先采用独立成分分析法,将患者功能磁共振成像数据分解成若干状态网络,获取个体独立成分。然后利用滑动窗口构建动态功能脑网络。最后将动态功能脑网络输入长短期记忆(long short-term memory,LSTM)网络,通过LSTM网络分类预测患者病情。在仿真平台上对基于独立成分分析法与LSTM的预测模型进行仿真,结果表明,基于独立成分分析法与LSTM的预测方法,可有效分类预测健康、患病、早期轻度认知障碍3类阿尔茨海默脑疾病,且具有较高的预测精度,平均分类准确率、敏感性、特异性和F₁值分别达到87.37%、87.48%、89.15%、87.65%,相较于支持向量机(support vector machine,SVM)、图神经网络(graph neural network,GNN)构建的预测模型,具有明显优势。由此得出,基于独立成分分析法与LSTM的预测方法,提高了脑疾病患者病情的预测精度,为脑疾病患者病情的精确预测奠定了基础。

Abstract

To improve the accuracy of predicting the condition of patients with brain diseases, a prediction model based on independent component analysis and LSTM is proposed with Alzheimer′s disease as the research object. The model is divided into three stages. Firstly, independent component analysis is used to decompose the patient′s functional magnetic resonance imaging data into several state networks and obtain individual independent components. Then sliding windows is used to construct dynamic functional brain networks. Finally, the dynamic functional brain network is input into a long short term memory (LSTM) network, which is used to classify and predict the patient′s condition. On the simulation platform, the prediction model based on independent component analysis and LSTM is simulated. The results show that the prediction method based on independent component analysis and LSTM can effectively classify and predict three types of Alzheimer′s brain diseases: healthy, diseased, and early mild cognitive impairment, with high prediction accuracy. The average classification accuracy, sensitivity, specificity, and F₁ score reaches 87.37%, 87.48%, 89.15%, and 87.65%, respectively. Compared with the prediction models constructed by SVM, GNN, etc., it has obvious advantages. From this, it can be concluded that the prediction method based on independent component analysis and LSTM improves the accuracy of predicting the condition of patients with brain diseases, laying the foundation for accurate prediction of the condition of patients with brain diseases.

关键词

深度学习 / LSTM网络 / 独立成分分析 / 病情预测 / 阿尔茨海默症 / 脑疾病

Key words

deep learning / LSTM network / independent component analysis / disease prediction / Alzheimer′s disease / brain disease