Abstract
Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding herbal materials. We used an association rule algorithm combined with network analysis and found useful and informative relationships between the symptoms and medicines.
Highlights
As a complementary medical system to Western medicine, traditional Korean medicine (TKM) has for thousands of years provided a unique theoretical and practical approach to the treatment of diseases
We used association rules to characterize the relationships between symptoms and herbal materials
We have used association rules and a couple of graphical approaches to reveal the patterns associated with symptoms and the related herbal materials
Summary
As a complementary medical system to Western medicine, traditional Korean medicine (TKM) has for thousands of years provided a unique theoretical and practical approach to the treatment of diseases. Based on traditional theory compiled through thousands of years of practice and research by TKM experts, a large amount of knowledge has accumulated in the form of ancient books and modern literature. Collecting materials and discovering rules on their uses, as is done in current practice, are time-consuming and error-prone. It usually takes several weeks for experts to manually process these documents for further medical tests to verify the effectiveness of a drug for specific symptoms. It is becoming harder to understand the interrelated roles of herbal materials in complex prescriptions
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