Application of data mining technology in excavating prevention and treatment experience of infectious diseases from famous herbalist doctors

Abstract

This study is to explore effect and significance of data mining technology (DMT) used in excavating prevention and treatment experience of infectious diseases from famous herbalist doctors (FHDs). DMT methods such as cluster analysis and association rules was applied to the study on FHDs literature on preventing and treating influenza, dysentery, tuberculosis, viral hepatitis and other infectious diseases in order to excavate the inner rules and refine the regular understanding. The result shows that cluster analysis is helpful to summarize the common understanding of experience including concept of syndrome differentiation, regulation of diagnosis and treatment, prescription characteristic, which based on FHDs' prevention and treatment of influenza (including A H1N1), viral hepatitis and other infectious diseases. Association rules greatly contributed to the judgment of relationship between etiology, syndrome, symptoms and herbal prescription of infectious diseases. In conclusion, DMT provides technical support for concise and inheritance of academic thought which originates from FHDs. DMT is of high application value in research of experience of FHDs and relevant literature, and is worth further discussion.