Study on Post-Stroke Dysphgia Intelligent Diagnostic Modeling Application Based on RBF
Network
CHEN Jie1, SU Chong2, XUE Yong3
1. Department of Acupuncture and Massage, Beijing Zhongguancun Hospital, Beijing 100190, China;
2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
3. Department of Rehabilitation, China-Japan Friendship Hospital, Beijing 100029, China
Abstract:Objective The intelligent model of the diagnosis of post-stroke dysphagia was built and described quantitatively based on
Radial Basis Function (RBF) network. Methods Firstly, two groups of 129 patients with no significant difference in age and sex were
selected from each group. 129 patients in the first group were assessed by the bedside clinical assessment scale for dysphagia along
with quantifying the data and results of expert diagnosis, and the diagnostic model was constructed based on RBF neural network;
129 patients in the second group were assessed by the scale and then input directly into the computer so that the computer
could output the diagnostic results; the second group was given to experts for diagnosis at the same time, and the results obtained by
experts were compared with those obtained by experts. The diagnostic results of the second group of computer outputs were
compared. Results We found that there was no statistical difference between the results of computer diagnosis and expert diagnosis
in the second group. Conclusion An intelligent diagnosis model of post-stroke dysphagia based on RBF neural network is
constructed, and expert diagnosis experience can be learned. Finally, compared with several typical machine learning methods,
the accuracy and advantages of the established RBF neural netwo rk model is verified.
陈捷1,宿翀2,薛勇3. 基于RBF神经网络的脑卒中后吞咽障碍智能诊断建模应用研究[J]. 中国医疗设备, 2019, 34(7): 20-23.
CHEN Jie1, SU Chong2, XUE Yong3. Study on Post-Stroke Dysphgia Intelligent Diagnostic Modeling Application Based on RBF
Network. China Medical Devices, 2019, 34(7): 20-23.