There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization (EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
|Publication status||Published - 13 Nov 2017|
|Event||IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017) - Kansas City, Mo., USA|
Duration: 13 Nov 2017 → 16 Nov 2017
|Conference||IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2017)|
|Period||13/11/17 → 16/11/17|
- Herb categorization, Heterogeneous information network, Clustering