Hierarchical Latent Class Models and Statistical Foundation for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain skeptical and even critical of TCM because a number of its shortcomings. One key shortcoming is the lack of a scientific foundation and hence objective diagnosis standards. When viewed as a black box, TCM diagnosis is simply a classifier that classifies patients into different classes based on their symptoms. A fundamental question is: Do those classes exist in reality? To seek an answer from the machine learning perspective, one would naturally use cluster analysis. Previous clustering methods are unable to handle the complexity of TCM. We have therefore developed a new clustering method in the form of hierarchical latent class (HLC) models. In this paper, we provide a brief review of HLC models and present a case study to demonstrate the possibility of establishing a statistical foundation for TCM using ...

• Type: Conference
• Year: 2007
• Where: AIME
• Authors: Nevin Lianwen Zhang, Shihong Yuan, Tao Chen, Yi Wang
• Keywords: AIME 2007, Artificial Intelligence, Clustering Method, Machine Learning, Traditional Chinese Medicine