Paper Info
Title
A conditional probabilistic model for joint analysis of symptoms, diseases, and herbs in traditional Chinese medicine patient records
Abstract
Traditional Chinese medicine (TCM) can provide important complementary medical care to modern medicine, and is widely practiced in China and many other countries. Unfortunately, due to its empirical nature and history of trial and error, effective diagnosis and prescription methods are not well-defined. This setback results in a significant challenge in retaining, sharing, and inheriting knowledge among physicians. In this paper, we propose a new asymmetric probabilistic model for the joint analysis of symptoms, diseases, and herbs in patient records to discover and extract latent TCM knowledge. We base our model on the comprehensive evaluation of modern medicine and TCM-specific symptoms in addition to herb prescriptions for particular diseases. Experimental results on a large dataset demonstrate the effectiveness of the proposed model for discovering useful knowledge and its potential clinical applications.
Year
DOI
Venue
2016
10.1109/BIBM.2016.7822553
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
conditional probabilistic model,symptoms,diseases,herbs,traditional Chinese medicine,patient records,modern medicine,China,asymmetric probabilistic model
Data modeling,Trial and error,Symptoms & diseases,Computer science,China,Traditional Chinese medicine,Artificial intelligence,Statistical model,Probabilistic logic,Machine learning,Medical prescription
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5090-1612-9
2
PageRank 
References 
Authors
0.36
0
7
Name
Order
Citations
PageRank
Sheng Wang1498.26
Edward Huang222.39
Runshun Zhang3219.37
Xiaoping Zhang471.89
Baoyan Liu521.38
Xuezhong Zhou620930.20
ChengXiang Zhai711908649.74