Medical Diagnosis System Based on Fast-weights Scheme

Abstract

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 proposed. MIMIC-III data sets are trained and tested, and the results are used to diagnosing. Variational recurrent neural network (VRNN) with FWs can better obtain the temporal features with partly missing data, and discriminant neural network (DNN) is for decision. Moreover, layer regularization (LN) avoids the overflow of loss function and stabilize the dynamic parameters of each layer. For the simulations, 10 laboratory tests were selected to predict 10 diseases, 1600 samples and 400 samples were used for training and testing, respectively. The 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.