Heterogeneous Information Network Based Clustering for Categorizations of Traditional Chinese Medicine Formula

Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. For TCM, herbal therapies are generally formula based, and individual herbs are rarely used. Unfortunately, due to the empirical nature of TCM, effective diagnosis and prescription methods are not well defined. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas.