Quantitative Research on Traditional Chinese Medicine Syndrome Based on TF-IDF Relative Entropy

VernacularTitle:基于TF-IDF相对熵的中医证候量化研究＊
Author: Jiangwei YU ; Quan YU ; Taizhen ZHANG ; Yu PENG
Publication Type:Journal Article
Keywords: Traditional Chinese medicine; TF-IDF; relative entropy; syndrome quantization; text mining
From: World Science and Technology-Modernization of Traditional Chinese Medicine 2015;(10):1986-1991
CountryChina
Language:Chinese
Abstract: This study proposed to use Term Frequency - Inverse Document Frequency (TF-IDF) relative entropy as knowledge representation method between symptoms and syndrome. TF-IDF was originated from text mining. It was an important method in the automatic text categorization. TF-IDF also represented the automatic categorization idea in traditional Chinese medicine (TCM) syndrome. It was based on the fact that the higher frequency of one symptom in specific syndrome, the stronger ability to distinguish this syndrome (TF); and the more wide range of one symptom in all syndrome, and the lower ability to distinguish a syndrome (IDF). It was verified with specific examples.