Medical Diagnosis System Based on Fast-weights Scheme

作     者：TIAN Shining LU Jihua GU Boyu WANG Huan 

作者机构：School of Information and ElectronicsBeijing Institute of TechnologyBeijing 100081China 

出 版 物：《Instrumentation》 (仪器仪表学报（英文版）)

年 卷 期：2020年第7卷第1期

页      面：51-57页

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by National Natural Science Foundation of China“Research on non-orthogonal multiple access technology for unauthorized transmission”(No.61771051) “Research on a new emergency positioning system for the integration of visible-light communication and MEMS inertial navigation”(No.61675025) 

主　　题：Fast Weights Scheme Discriminant Neural Network Variational Recurrent Neural Network Diagnosis Accuracy 

摘      要：Clinical examination data often have the features of carrying vague information,missing data and incomplete examination records,which lead to higher probabilities of misdiagnosis.A variational recursive-discriminant joint model with fast weights(FWs)scheme is ***-III data sets are trained and tested,and the results are used to *** recurrent neural network(VRNN)with FWs can better obtain the temporal features with partly missing data,and discriminant neural network(DNN)is for ***,layer regularization(LN)avoids the overflow of loss function and stabilize the dynamic parameters of each *** the simulations,10 laboratory tests were selected to predict 10 diseases,1600 samples and 400 samples were used for training and testing,*** test accuracy of disease diagnosis without FWs is 72.55%,and that with FWs is 85.80%.Simulations reveal that the FWs mechanism can effectively optimize the system model,abstracting the features for diagnose,and significantly improve the accuracy of decision-making.