Paper:
Language: English:

A Proposed Model of Diagnosis and Prescription in Oriental Medicine Using RBF Neural Networks

Cao Thang^, Eric W. Cooper^, Yukinobu Hoshino^,
Katsuari Kamei^*, and Nguyen Hoang Phuong^**

^Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
^21^st Century Center of Excellence Program, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
^College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan
^****Center of Health Information Technology, Ministry of Health of Vietnam, The 9 floors Building, 134 Nui Truc Lane, Ba Dinh Dist., Hanoi, Vietnam

Received: June 14, 2005

Accepted: October 9, 2005

Keywords: decision support system, oriental medicine, RBF neural networks

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.4 pp. 458-464, 2006

Abstract

In this paper, we present a computing model for diagnosis and prescription in oriental medicine. Inputs to the model are severities of symptoms observed on patients and outputs from the model are a diagnosis of disease states and treatment herbal prescriptions. First, having used rule inference with a Gaussian distribution, the most serious disease state in which the patient appears to be infected is determined. Next, an herbal prescription written in suitable herbs with reasonable amounts for treating the infected disease state is given by RBF neural networks. Finally, we show some experiments and their evaluations, and then describe our future works.

Reference

[1] T. Tran, D. N. Pham, and B. C. Hoang, “Hanoi Medical University – Lectures in Oriental Medicine,” Medicine Pub., Hanoi, 2002.

[2] J. Durkin, “Expert System – Design and Development,” Prentice Hall Inc., New York, 1994.

[3] F. A. Maysam, D. G. von Keyserlingk, and D. A. Linkens, “Mahdi Mahfouf: Survey of utilization of fuzzy technology in Medicine and Healthcare,” Fuzzy Sets and Systems, 120, pp. 331-349, 2001.

[4] H. P. Nguyen, T. T. Nguyen, C. Thang, and T. H. Duong, “Building a fuzzy expert system for syndrome differentiation in the oriental traditional medicine,” Proceedings of the Hanoi International Symposium on Medical Informatics and Fuzzy Technology (MIF’99), pp. 436-441, 1999.

[5] H. P. Nguyen, S. Pratit, and K. Hirota, “Fuzzy Modeling for Modifying Standard Prescriptions of Oriental Traditional Medicine,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.3, pp. 339-347, 2003.

[6] M. B. Serrano, C. Sierra, and R. Lopez de Mantaras, “RENOIR: An expert system using fuzzy logic for rheumatology diagnosis,” Internat. J. Intell. Systems, 9 (11), pp. 985-1000, 1994.

[7] R. Dybowski, and V. Gant, “Clinical applications of Artificial neuralnetworks,” Cambridge University Press, 2001.

[8] C. Thang, E. W. Cooper, Y. Hoshino, and K. Kamei, “A Proposed Model of Soft Computing in Diagnosing Diseases and Prescribing Herbal Prescriptions by Oriental Medicine,” Proceedings of the First International Conference on Complex Medical Engineering (CME2005), pp. 904-908, 2005.

[9] C. M. Bishop, “Neural Networks for Pattern Recognition,” Oxford University Press, 2005.

[10] S. Haykin, “Neural Networks,” A Comprehensive Foundation, Prentice Hall, 1994.

[11] A. Bezerianos, S. Papadimitriou, and D. Alexopoulos, “Radial basis function neural networks for the characterization of heart rate variability dynamics,” Artificial Intelligence in Medicine, 15(3), pp. 215-234, 1999.