A Traditional Chinese Medicine Prescription Recommendation method based on Mutual Information Clustering

Abstract

Traditional Chinese medicine (TCM) data is the main knowledge resource of TCM, which contains a wealth of clinical experience knowledge. Machine learning has made remarkable achievements in natural language processing. As the carrier of TCM knowledge and information stored in the form of text, using machine learning method to study these TCM data can save a lot of manpower cost, improve the objectivity of TCM, promote TCM related knowledge better, and have certain guiding significance for the research of TCM human engineering experiment. This paper proposes a recommendation algorithm based on mutual information clustering. Its core idea is calculating mutual information between two symptoms, and set symptom "relatives and friends group", after getting the symptom clustering results of mutual information, then combine the clustering results and search algorithm to achieve the effect of recommendation and filtering. Experimental results show that the proposed method is effective.