2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS) (2010)
Oct. 12, 2010 to Oct. 15, 2010
J. H. K. Wong , Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
W. W. K. Lin , Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
A. K. Y. Wong , Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
T. S. Dillon , DEBI Inst., Curtin Univ., Perth, WA, Australia
A novel approach, which is based on artificial neural network (ANN) by backpropagation, for fast and trusted herbal ingredient discoveries, is proposed. It is fast, because different ANN modules can be executed in parallel, and the ANN results are trustworthy, because they can be verified by TCM domain experts in real clinical environments. The ANN is able to learn the relationship between herbal ingredients and the set of information given (e.g. symptoms and illnesses). The ANN output is called the relevance index (RI), which conceptually associates two TCM entities (e.g. U and V) in a 2-D or 3-D manner (D for dimension). RI is the quantified P(U $/cap$ V) part of P(U $/cup$ V) = P(U) + P(V) - P(U $/cap$ V), an IT (information technology) formalism in which P stands for probability. The interpretation of P(U $/cap$ V) adheres to TCM formalism(s).
traditional chinese medicine, artificial neural network, backpropagation, trusted herbal ingredient discoveries, parallel execution, TCM, real clinical environments, relevance index
J. H. Wong, W. W. Lin, A. K. Wong and T. S. Dillon, "Artificial neural network for herbal ingredient discoveries," 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS), Perth, WA, 2010, pp. 209-214.