KISTCM: knowledge discovery system for traditional Chinese medicine
| dc.contributor.author | Qiao, Shaojie | |
| dc.contributor.author | Tang, Changjie | |
| dc.contributor.author | Jin, Huidong (Warren) | |
| dc.contributor.author | Peng, Jing | |
| dc.contributor.author | David, Darren | |
| dc.contributor.author | Han, Nan | |
| dc.date.accessioned | 2015-12-10T22:39:37Z | |
| dc.date.issued | 2010 | |
| dc.date.updated | 2016-02-24T10:18:55Z | |
| dc.description.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. | |
| dc.identifier.issn | 1573-7497 | |
| dc.identifier.uri | http://hdl.handle.net/1885/57256 | |
| dc.publisher | Springer | |
| dc.source | Applied Intelligence | |
| dc.subject | Keywords: Alternative methods; Data analysis; Data mining techniques; Dependency relationship; Design and Development; Dimension reduction algorithm; Discovery algorithm; Gene expression programming; Hidden knowledge; Hidden rules; Including rule; Knowledge Discove Data mining; Herbal formula; Knowledge discovery; Traditional Chinese medicine | |
| dc.title | KISTCM: knowledge discovery system for traditional Chinese medicine | |
| dc.type | Journal article | |
| local.bibliographicCitation.lastpage | 363 | |
| local.bibliographicCitation.startpage | 346 | |
| local.contributor.affiliation | Qiao, Shaojie, Sichuan University | |
| local.contributor.affiliation | Tang, Changjie, Sichuan University | |
| local.contributor.affiliation | Jin, Huidong (Warren), College of Engineering and Computer Science, ANU | |
| local.contributor.affiliation | Peng, Jing, Peking University | |
| local.contributor.affiliation | David, Darren, Univeristy of San Diego | |
| local.contributor.affiliation | Han, Nan, Chengdu University of Traditional Chinese Medicine | |
| local.contributor.authoremail | u1817131@anu.edu.au | |
| local.contributor.authoruid | Jin, Huidong (Warren), u1817131 | |
| local.description.embargo | 2037-12-31 | |
| local.description.notes | Imported from ARIES | |
| local.identifier.absfor | 080109 - Pattern Recognition and Data Mining | |
| local.identifier.absseo | 970108 - Expanding Knowledge in the Information and Computing Sciences | |
| local.identifier.ariespublication | U3594520xPUB393 | |
| local.identifier.citationvolume | 32 | |
| local.identifier.doi | 10.1007/s10489-008-0149-4 | |
| local.identifier.scopusID | 2-s2.0-77952010720 | |
| local.identifier.thomsonID | 000277282600008 | |
| local.identifier.uidSubmittedBy | U3594520 | |
| local.type.status | Published Version |
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