Résumé
Objective To develop an effective decision tree management model for smart triage of nervous system diseases based on artificial neural networks and Bayesian decision theory. Methods Bayesian decision theory was used as the theoretical basis, and convolutional neural network was used to complete the rapid specialist / sub-specialist machine learning. For the specialist or sub-specialist triage data, circular neural network and Bayesian algorithm were performed to complete the probability distribution and convergence of disease symptoms and diagnosis. Results The decision tree management model and theoretical demonstration were established. According to the characteristics of the transfer learning, the rapid learning of nervous system diseases and accurate triage system, and the remote smart triage system were successfully constructed. Conclusions The management model could provide theoretical references for further use, and alleviate to some extent the currently high rate of outpatient appointment withdrawal and changes.