基于知识表示向量的可解释深度学习模型及其疾病预测应用  

Interpretable Deep Learning Model Based on Knowledge Representation Vectors and Its Application in Disease Prediction

作　　者：徐鹤[1,2] 郑群力 谢作玲 程海涛[1,2] 李鹏 季一木[1,2] XU He;ZHENG Qunli;XIE Zuoling;CHENG Haitao;LI Peng;JI Yimu(School of Computer Science/School of Software/School of Cyberspace Security,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Jiangsu HPC and Intelligent Processing Engineer Research Center,Nanjing 210023,China;Department of Endocrinology,Zhongda Hospital Southeast University,Nanjing 210009,China)

机构地区：[1]南京邮电大学计算机学院/软件学院/网络空间安全学院,南京210023 [2]江苏省高性能计算与智能处理工程研究中心,南京210023 [3]东南大学附属中大医院内分泌科,南京210009

出　　处：《数据采集与处理》2023年第4期777-791,共15页Journal of Data Acquisition and Processing

基　　金：江苏省科技支撑计划项目(BE2019740);江苏省六大人才高峰高层次人才项目(RJFW-111)。

摘　　要：近年来,深度学习方法广泛应用于各种疾病预测任务,甚至在其中一些方面超过了人类专家。然而,算法的黑盒性质限制了其临床应用。对此,本文结合知识表示学习和深度学习方法构建了一种融入知识表示向量的可解释深度学习模型。该模型首先依据体检指标正常范围构建体检指标与检测值之间的关系图,并通过基于知识表示学习的深度学习模型对人体体检指标与检测值关系图进行编码,然后将患者体检数据表示为向量,输入到构建的自注意力机制和卷积神经网络构建的分类器中来实现疾病预测。将模型应用于糖尿病预测实验中,其准确率和召回率均优于对比的机器学习方法。与表现较优的随机森林算法相比,模型的准确率和召回率分别提升了0.81%和5.21%。实验结果表明,通过可解释性方法将知识表示学习和深度学习技术融合应用于糖尿病预测,可以达到对糖尿病的早期发现与辅助诊断的目的。In recent years,deep learning methods have been widely applied to various disease prediction tasks,even surpassing human experts in some aspects.However,the black box nature of the algorithm limits its clinical application.In this paper,the knowledge representation and reasoning learning and deep learning methods are combined to build an interpretable deep learning model incorporating knowledge representation and reasoning vectors.The model first builds a relationship graph between physical examination indicators and test values according to the normal range of physical examination indicators,and the relationship graph between physical examination indicators and test values is coded through the deep learning model based on knowledge representation and reasoning learning.Then,the patients’physical examination data are expressed as vectors,which are input into the self-attention mechanism and the classifier constructed by convolutional neural network to realize the disease prediction.When the model is applied to the prediction experiment of diabetes,the accuracy and recall of the model are better than those of the comparative machine learning methods.Compared with the random forest algorithm,the accuracy and recall are also improved by 0.81%and 5.21%,respectively.Experimental results show that the application of knowledge representation and reasoning learning and deep learning technological convergence to diabetes prediction through interpretable methods can achieve the purpose of early detection and auxiliary diagnosis of diabetes.

关 键 词：疾病预测 知识表示学习 深度学习 自注意力机制 卷积神经网络 可解释性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]