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KISTCM: knowledge discovery system for traditional Chinese medicine

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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.

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Authors and Affiliations

  1. School of Computer Science, Sichuan University, Chengdu, 610065, China

    Shaojie Qiao & Changjie Tang

  2. School of Computing, National University of Singapore, Singapore, 117590, Singapore

    Shaojie Qiao

  3. Mathematical and Information Sciences, The Commonwealth Scientific and Industrial Research Organisation, North Road, ANU Campus, Canberra, ACT, 2601, Australia

    Huidong Jin

  4. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Jing Peng

  5. Department of Mathematics and Computer Science, University of San Diego, San Diego, 92110, USA

    Darren Davis

  6. School of Chinese Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China

    Nan Han

Authors
  1. Shaojie Qiao
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  2. Changjie Tang
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  3. Huidong Jin
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  4. Jing Peng
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  5. Darren Davis
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  6. Nan Han
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Correspondence to Shaojie Qiao.

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Qiao, S., Tang, C., Jin, H. et al. KISTCM: knowledge discovery system for traditional Chinese medicine. Appl Intell 32, 346–363 (2010). https://doi.org/10.1007/s10489-008-0149-4

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  • DOI: https://doi.org/10.1007/s10489-008-0149-4

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