The Research on Neural Network Diagnosis and Treatment System of Child Mental Health Disorders

Abstract:

The research of diagnosis and treatment system of child mental health disorders is based on artificial neural network and expert system. It combines the diagnosis standard of ICD 10, DSM IV with 40 years clinical experiences and knowledge of senior child psychiatrists. It also combines computer science with child psychiatry, child psychology, psychological estimate, psychological therapy and so on. The learning samples come from the epidemiological data in more than a dozen nationwide hospitals. The correct rate of system diagnosis is 99%. The system can diagnose 61 kinds of child mental health disorders and give a treatment method suggestion.

• Periodical: Applied Mechanics and Materials (Volume 707)
• Pages: 188-192
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.707.188
• Online since: December 2014
• Authors: Bing Mei Chen*, Han Wen Zhou, Xiao Ping Fan, Xue Rong Li, Zhi Ming Zhou
• Keywords: Artificial Intelligence Expert System, Child Mental Health Disorders, Intelligent Diagnosis, Neural Network

References

[1] Quteishat, et al. A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on , 2010 , 40(3): 641 - 650.

DOI: 10.1109/tsmca.2010.2043948

[2] Liu Jicheng; Niu Dongxiao, A novel recurrent neural network forecasting model for power intelligence center, Journal of Central South University of Technology, 2008. 15: 726−732.

DOI: 10.1007/s11771-008-0134-4

[3] E. Alba and J. F. Chicano, Training neural networks with GA hybrid algorithms, in Proc. of GECCO 2004, ser. LNCS, K. Deb et al., Ed., Springer Verlag, Berlin, Germany, 2004: 852–863.

DOI: 10.1007/978-3-540-24854-5_87

[4] Zhao Jianli; Liu Jiaomin, et al. Study on the Ripple Effects of Requirement Evolution Based on Feedforward Neural Network, Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference, 2010: 677 – 680.

DOI: 10.1109/icmtma.2010.570

[5] Jin Hua, Application of fuzzy neural network in multi-maneuvering target tracking, Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, 2010: 92 – 95.

DOI: 10.1109/car.2010.5456770

[6] Maojun Cao, et al. Double Chains Quantum Genetic Algorithm with Application in Training of Process Neural Networks, Education Technology and Computer Science (ETCS), 2010 Second International Workshop on , 2010 : 19 – 22.

DOI: 10.1109/etcs.2010.88

[7] Zhiwei Wang, etc. Particle Swarm Optimization and Neural Network Application for QSAR, Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS'04), 0-7695-2132-0/04 (C) 2004 IEEE.

DOI: 10.1109/ipdps.2004.1303214

[8] Han L, Shi L P, Xu Z G. A pruning algorithm for RBF neural network based on rough sets [J]. Information and Control, 2007, 36: 604-609.

[9] Ma T H, Zhao Y W. Attribute reduction algorithm based on reduction pruning [J]. Computer Engineering, 2007, 18: 56-58.

[10] Xu X H, Zhu J, Guo Q. Decision tree dynamic pruning method based on minimum description length in speech recognition [J]. Acta Acustica, 2006(4): 370-375.

[11] ZHANG Hong-liang, ZOU Zhong, LI Jie. Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods [J]. Journal of Central South University of Technology, 2008, 15(1): 39−43.

DOI: 10.1007/s11771-008-0009-8