KISTCM: knowledge discovery system for traditional Chinese medicine

Authors:
Shaojie Qiao;Changjie Tang;Huidong Jin;Jing Peng;Darren Davis;Nan Han
Affiliations:
School of Computer Science, Sichuan University, Chengdu, China 610065 and School of Computing, National University of Singapore, Singapore, Singapore 117590;School of Computer Science, Sichuan University, Chengdu, China 610065;Mathematical and Information Sciences, The Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia 2601;School of Electronics Engineering and Computer Science, Peking University, Beijing, China 100871;Department of Mathematics and Computer Science, University of San Diego, San Diego, USA 92110;School of Chinese Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China 610075
Venue:
Applied Intelligence
Year:
2010

Citing 7
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Text mining for finding functional community of related genes using TCM knowledge

PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Methodological review: Knowledge discovery in traditional Chinese medicine: State of the art and perspectives

Artificial Intelligence in Medicine
Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks

Artificial Intelligence in Medicine
Combining an order-semisensitive text similarity and closest fit approach to textual missing values in knowledge discovery

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text

Artificial Intelligence in Medicine
Uniqueness of medical data mining

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.