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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 317803, 11 pages
http://dx.doi.org/10.1155/2013/317803
Research Article

Ontology-Oriented Diagnostic System for Traditional Chinese Medicine Based on Relation Refinement

1College of Computer Science, Zhejiang University, Hangzhou 310027, China
2China Academy of Chinese Medical Sciences, Beijing 100700, China

Received 26 October 2012; Revised 31 December 2012; Accepted 2 January 2013

Academic Editor: Alejandro Rodríguez González

Copyright © 2013 Peiqin Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Although Chinese medicine treatments have become popular recently, the complicated Chinese medical knowledge has made it difficult to be applied in computer-aided diagnostics. The ability to model and use the knowledge becomes an important issue. In this paper, we define the diagnosis in Traditional Chinese Medicine (TCM) as discovering the fuzzy relations between symptoms and syndromes. An Ontology-oriented Diagnosis System (ODS) is created to address the knowledge-based diagnosis based on a well-defined ontology of syndromes. The ontology transforms the implicit relationships among syndromes into a machine-interpretable model. The clinical data used for feature selection is collected from a national TCM research institute in China, which serves as a training source for syndrome differentiation. The ODS analyzes the clinical cases to obtain a statistical mapping relation between each syndrome and associated symptom set, before rechecking the completeness of related symptoms via ontology refinement. Our diagnostic system provides an online web interface to interact with users, so that users can perform self-diagnosis. We tested 12 common clinical cases on the diagnosis system, and it turned out that, given the agree metric, the system achieved better diagnostic accuracy compared to nonontology method—92% of the results fit perfectly with the experts’ expectations.