KISTCM: knowledge discovery system for traditional Chinese medicine

Objective: Traditional Chinese Medicine (TCM) provides an alternative method for achieving and maintaining good health. Due to the increasing prevalence of TCM and the large volume of TCM data accumulated though thousands of years, there is an urgent need to efficiently and effectively explore this information and its hidden rules with knowledge discovery in database (KDD) techniques. This paper describes the design and development of a knowledge discovery system for TCM as well as the newly proposed KDD techniques integrated in this system.Methods: A novel Knowledge dIscovery System for TCM (KISTCM) is developed by incorporating several data mining techniques, primarily including a medicine dependency relationship discovery algorithm, an efficacy dimension reduction algorithm based on neural networks, a method for exploring the relationships between formulae and syndromes using gene expression programming (GEP), and an approach for discovering the properties in terms of nature, taste and meridian based on the herbal dosage by employing the effect degree function to calculate the effect of each property.Results: Representative experimental cases are used to evaluate the system performance. Encouraging results are obtained, including rules previously unknown to algorithm designers and experiment runners. Experiments demonstrate that KISTCM has powerful knowledge discovery and data analysis capabilities, and is a useful tool for discovering the underlying rules in formulae. Our proposed techniques successfully discover hidden knowledge from TCM data, which is a new direction in knowledge discovery. From TCM experts’ perspective, the accuracy of data analysis for KISTCM is an improvement, and these results compare favorably to other existing TCM data mining techniques. The system could be expected to be useful in the practice of TCM, e.g., assisting TCM physicians in prescribing formulae or automatically distinguishing between minister and assistant herbs in a formula.

[1]  Zhou Lu.  Fuzzy clustering analysis of Chinese herbs for relieving exterior syndrome , 2004 .

[2]  Russ B. Altman,et al.  AI in Medicine: The Spectrum of Challenges from Managed Care to Molecular Medicine , 1999, AI Mag..

[3]  Jean-François Boulicaut,et al.  Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases , 2004 .

[4]  Tang Chang-jie,et al.  A Chinese Traditional Medicine Prescription Effect Reduction Algorithm Based on Artificial Neural Network and Property Distance Matrix , 2006 .

[5]  Yu Xian.  Mining formula-syndrome relationship in traditional Chinese medicine with gene expression programming , 2005 .

[6]  Zhong-Mei Zhou,et al.  Mining Frequent Maximum Patterns with Constraint , 2004 .

[7]  Cungen Cao,et al.  Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text , 2004, Artif. Intell. Medicine.

[8]  Xiang Zheng,et al.  A 3-Stage Voting Algorithm for Mining Optimal Ingredient Pattern of Traditional Chinese Medicine , 2003 .

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  Zhao Ming.  Mining the Compatibility Law of Multidimensional Medicines Based on Dependence Mode Sets , 2007 .

[11]  Changjie Tang,et al.  TCMiner: A High Performance Data Mining System for Multi-dimensional Data Analysis of Traditional Chinese Medicine Prescriptions , 2004, ER.

[12]  Zhaohui Wu,et al.  Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks , 2007, Artif. Intell. Medicine.

[13]  B Zupan,et al.  Data mining techniques and applications in medicine. , 1999, Artificial intelligence in medicine.

[14]  Nada Lavrac,et al.  Selected techniques for data mining in medicine , 1999, Artif. Intell. Medicine.

[15]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[16]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[17]  Peter A. Bath,et al.  Data mining in health and medical information , 2005, Annu. Rev. Inf. Sci. Technol..

[18]  Junli Chen,et al.  Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge , 2004, PKDD.

[19]  Zhaohui Wu,et al.  Knowledge discovery in traditional Chinese medicine: State of the art and perspectives , 2006, Artif. Intell. Medicine.

[20]  Krzysztof J. Cios,et al.  Uniqueness of medical data mining , 2002, Artif. Intell. Medicine.

[21]  Zhaohui Wu,et al.  Combining an Order-Semisensitive Text Similarity and Closest Fit Approach to Textual Missing Values in Knowledge Discovery , 2005, KES.