Method of Refining Knowledge in Oriental Medicine by Sample Cases

Chang Hoon LEE; Moon Hae KIM; Jung Wan CHO

Method of Refining Knowledge in Oriental Medicine by Sample Cases

Chang Hoon LEE, Moon Hae KIM, Jung Wan CHO

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In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.

Publication: IEICE TRANSACTIONS on Information Vol.E76-D No.2 pp.284-295

Publication Date: 1993/02/25

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Type of Manuscript: PAPER

Category: Medical Electronics and Medical Information

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Chang Hoon LEE, Moon Hae KIM, Jung Wan CHO, "Method of Refining Knowledge in Oriental Medicine by Sample Cases" in IEICE TRANSACTIONS on Information, vol. E76-D, no. 2, pp. 284-295, February 1993, doi: .
Abstract: In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_2_284/_p

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@ARTICLE{e76-d_2_284,
author={Chang Hoon LEE, Moon Hae KIM, Jung Wan CHO, },
journal={IEICE TRANSACTIONS on Information},
title={Method of Refining Knowledge in Oriental Medicine by Sample Cases},
year={1993},
volume={E76-D},
number={2},
pages={284-295},
abstract={In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - Method of Refining Knowledge in Oriental Medicine by Sample Cases
T2 - IEICE TRANSACTIONS on Information
SP - 284
EP - 295
AU - Chang Hoon LEE
AU - Moon Hae KIM
AU - Jung Wan CHO
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1993
AB - In general, the work on developing an expert system has relied on domain experts to provide all domain-specific knowledge. The method for acquiring knowledge directly from experts is inadequate in oriental medicine because it is hard to find an appropriate expert and the development cost becomes too high. Therefore, we have developed two effective methods for acquiring knowledge indirectly from sample cases. One is to refine a constructed knowledge base by using sample cases. The other is to train a neural network by using sample cases. To demonstrate the effectiveness of our methods, we have implemented two prototype systems; the Oriental Medicine Expert System (OMES) and the Oriental Medicine Neural Network (OMNN). These systems have been compared with the system with the knowledge base built directly by domain experts (OLDS). Among these systems, OMES are considered to be superior to other systems in terms of performances, development costs, and practicalness. In this paper, we present our methods, and describe our experimental and comparison results.
ER -