论文标题
thcluster：草药补充剂分类用于精确中医
THCluster: herb supplements categorization for precision traditional Chinese medicine
论文作者
论文摘要
全世界对传统和补充医学的需求一直在持续。传统中医（TCM）中的一个基本和重要主题是优化处方并从TCM数据中检测草药规律。在本文中，我们提出了一个新型的聚类模型，以解决这种普遍的草药分类问题，处方优化的关键任务和草药规律性。该模型利用随机步行方法，贝叶斯规则和期望最大化（EM）模型在异质信息网络上有效地完成聚类分析。我们在现实世界数据集上进行了广泛的实验，并将我们的方法与其他算法和专家进行了比较。实验结果证明了所提出的模型在发现草药的有用分类及其潜在的临床表现方面的有效性。
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.
