基于层次注意力机制的多任务疾病进展模型  

MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism

作　　者：潘祖江 刘宁 张伟[2] 王建勇[1] PAN Zu-jiang;LIU Ning;ZHANG Wei;WANG Jian-yong(Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;School of Computer Science and Technology,East China Normal University,Shanghai 200333,China)

机构地区：[1]清华大学计算机科学与技术系,北京100084 [2]华东师范大学计算机科学与技术学院,上海200333

出　　处：《计算机科学》2020年第9期185-189,共5页Computer Science

基　　金：国家自然科学基金重点项目(61532010)。

摘　　要：阿尔茨海默症是一种不可逆的神经退化疾病,由于脑组织的退化而产生的严重的认知问题。目前已有许多临床实验和研究计划来研究阿尔茨海默症的病理学,这些实验和计划会产生一些可以用来分析的数据。文中着重结合多种临床特征,对阿尔茨海默症进行自动诊断,并预测潜在的预后风险,进而提出了一个基于层次注意力机制的多任务疾病进展模型。该模型将疾病自动诊断任务作为主任务,疾病预后预测任务作为辅任务,以提升模型的泛化能力,进而提升疾病自动诊断任务的效果。其应用了两层的注意力机制,注意力分别应用在特征层和就诊记录层,使得模型可以对不同的特征以及不同的就诊记录有不同的注意力。在ADNI(Alzheimer’s Disease Neuroimaging Initiative)数据集上进行实验,并将所提模型与多个基准模型进行比较,实验结果表明,提出的模型具有更好的效果,为临床实际应用提供了更好的鲁棒性。Alzheimer’s disease(AD)is an irreversible neurodegenerative disease.The degeneration of brain tissue causes serious cognitive problems and eventually leads to death.There are many clinical trials and research projects to study AD pathology and produce some data for analysis.This paper focuses on the diagnosis of AD and the prediction of potential prognosis in combination with a variety of clinical features.In this paper,a multi-task disease progression model based on hierarchical attention mechanism is proposed.The task of disease automatic diagnosis is regarded as the main task,and the task of disease prognosis is regarded as the auxiliary task to improve the generalization ability of the model,and then improve the performance of disease automatic diagnosis task.In this paper,two layers of attention mechanism are applied in the feature layer and the medical record layer respectively,so that the model can pay different attention to different features and different medical records.The validation experiment is carried out on ADNI(Alzheimer’s Disease Neuroimaging Initiative)dataset.Compared with several benchmark models,the experimental results show that the proposed method has better performance and provides better robustness for clinical application.

关 键 词：注意力机制 多任务学习 自动诊断 预后预测 阿尔茨海默症 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]