Abstract

Info

Title:: Clinical decision support system for diabetic peripheral neuropathy based on Co-LSTM-FC network

Author(s):: LIU Yuliang1; DING Yongchuan1; GUO Yujia1; ZHAO Geng2; YANG Weiming1; 1. School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300202, China 2. Department of Laboratory, Metabolic Disease Hospital of Tianjin Medical University, Tianjin 300070, China

Keywords:: Keywords: diabetic peripheral neuropathy clinical decision support system FC-LSTM network ConvLSTM network

Abstract:: Abstract: A clinical decision support system (DPN-CDSS) based on Co-LSTM-FC network is proposed for the early prediction of diabetic peripheral neuropathy (DPN), thereby assisting doctors in the early DPN diagnosis and decision-making. Co-LSTM-FC network model innovatively uses FC-LSTM network and ConvLSTM network to jointly extract the features from the clinical data, which reduces the weight deviation that occurs in the calculation of a single model. Meanwhile, the fully connected neural network is adopted to classify the characteristics of the disease for improving the accuracy of the prediction model. The accuracy, specificity, F1 value, G-mean value and AUC value of the proposed method for DPN prediction are 95.51%, 94.24%, 95.06%, 95.08% and 94.37%, respectively, and the accuracy is higher as compared with other models. Moreover, DPN-CDSS user interface which includes user login, data input and result display interface is convenient for doctors and patients to use. The system can screen for DPN in advance, assist doctors in the initial diagnosis, and improve the efficiency of diagnosis and treatment.