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http://dx.doi.org/10.15188/kjopp.2020.08.34.4.159

Analysis of Symptoms-Herbs Relationships in Shanghanlun Using Text Mining Approach  

Jang, Dongyeop (Department of Physiology, College of Korean Medicine, Gachon University)
Ha, Yoonsu (College of Korean Medicine, Gachon University)
Lee, Choong-Yeol (Department of Physiology, College of Korean Medicine, Gachon University)
Kim, Chang-Eop (Department of Physiology, College of Korean Medicine, Gachon University)
Publication Information
Journal of Physiology & Pathology in Korean Medicine / v.34, no.4, 2020 , pp. 159-169 More about this Journal
Abstract
Shanghanlun (Treatise on Cold Damage Diseases) is the oldest document in the literature on clinical records of Traditional Asian medicine (TAM), on which TAM theories about symptoms-herbs relationships are based. In this study, we aim to quantitatively explore the relationships between symptoms and herbs in Shanghanlun. The text in Shanghanlun was converted into structured data. Using the structured data, Term Frequency - Inverse Document Frequency (TF-IDF) scores of symptoms and herbs were calculated from each chapter to derive the major symptoms and herbs in each chapter. To understand the structure of the entire document, principal component analysis (PCA) was performed for the 6-dimensional chapter space. Bipartite network analysis was conducted focusing on Jaccard scores between symptoms and herbs and eigenvector centralities of nodes. TF-IDF scores showed the characteristics of each chapter through major symptoms and herbs. Principal components drawn by PCA suggested the entire structure of Shanghanlun. The network analysis revealed a 'multi herbs - multi symptoms' relationship. Common symptoms and herbs were drawn from high eigenvector centralities of their nodes, while specific symptoms and herbs were drawn from low centralities. Symptoms expected to be treated by herbs were derived, respectively. Using measurable metrics, we conducted a computational study on patterns of Shanghanlun. Quantitative researches on TAM theories will contribute to improving the clarity of TAM theories.
Keywords
Shanghanlun; Text mining; Symptoms-herbs relationships;
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Times Cited By KSCI : 6  (Citation Analysis)
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