The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Efficient Learning of Hierarchical Latent Class Models
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A self-learning expert system for diagnosis in traditional Chinese medicine
Expert Systems with Applications: An International Journal
TCM-Grid: weaving a medical grid for traditional Chinese medicine
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Mining both associated and correlated patterns
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Structural learning of graphical models and its applications to traditional chinese medicine
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
NNF: an effective approach in medicine paring analysis of traditional chinese medicine prescriptions
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Text mining for clinical chinese herbal medical knowledge discovery
DS'05 Proceedings of the 8th international conference on Discovery Science
Ontology development for unified traditional Chinese medical language system
Artificial Intelligence in Medicine
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
Latent variable discovery in classification models
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine
Computers in Biology and Medicine
Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine
Computer Methods and Programs in Biomedicine
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
Semantic web development for traditional Chinese medicine
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Artificial Intelligence in Medicine
International Journal of Knowledge Engineering and Data Mining
KISTCM: knowledge discovery system for traditional Chinese medicine
Applied Intelligence
Methodological Review: Text mining for traditional Chinese medical knowledge discovery: A survey
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Discovery of regularities in the use of herbs in traditional chinese medicine prescriptions
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
The impact of feature representation to the biclustering of symptoms-herbs in TCM
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
A method for finding groups of related herbs in traditional chinese medicine
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Efficient classifiers for multi-class classification problems
Decision Support Systems
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers of KES2012-Part 1 of 2
Journal of Biomedical Informatics
Hi-index | 0.00 |
Objective: As a complementary medical system to Western medicine, traditional Chinese medicine (TCM) provides a unique theoretical and practical approach to the treatment of diseases over thousands of years. Confronted with the increasing popularity of TCM and the huge volume of TCM data, historically accumulated and recently obtained, there is an urgent need to explore these resources effectively by the techniques of knowledge discovery in database (KDD). This paper aims at providing an overview of recent KDD studies in TCM field. Methods: A literature search was conducted in both English and Chinese publications, and major studies of knowledge discovery in TCM (KDTCM) reported in these materials were identified. Based on an introduction to the state of the art of TCM data resources, a review of four subfields of KDTCM research was presented, including KDD for the research of Chinese medical formula, KDD for the research of Chinese herbal medicine, KDD for TCM syndrome research, and KDD for TCM clinical diagnosis. Furthermore, the current state and main problems in each subfield were summarized based on a discussion of existing studies, and future directions for each subfield were also proposed accordingly. Results: A series of KDD methods are used in existing KDTCM researches, ranging from conventional frequent itemset mining to state of the art latent structure model. Considerable interesting discoveries are obtained by these methods, such as novel TCM paired drugs discovered by frequent itemset analysis, functional community of related genes discovered under syndrome perspective by text mining, the high proportion of toxic plants in the botanical family Ranunculaceae disclosed by statistical analysis, the association between M-cholinoceptor blocking drug and Solanaceae revealed by association rule mining, etc. It is particularly inspiring to see some studies connecting TCM with biomedicine, which provide a novel top-down view for functional genomics research. However, further developments of KDD methods are still expected to better adapt to the features of TCM. Conclusions: Existing studies demonstrate that KDTCM is effective in obtaining medical discoveries. However, much more work needs to be done in order to discover real diamonds from TCM domain. The usage and development of KDTCM in the future will substantially contribute to the TCM community, as well as modern life science.