Summarizing Professor Chen Ruquan's Therapeutic Experience of Thyroid Disease Based on Machine Learning

Abstract

Objectives: To summarize the clinical experience of Professor Chen Ruquan in the treatment of thyroid disease. Technology or Method: All published literatures about Professor Chen Ruquan in the treatment of thyroid disease were searched and collected through computer retrieval; a comprehensive literature search was conducted from four databases including CNKI, VIP, Wan-Fang Database, and China Biological Database. The clinical cases were extracted by applying NLP technology from the included literatures. The machine learning algorithms such as Association rules and Hierarchical Cluster analysis were applied through R software 3.6. Results: A total of 71 clinical cases for thyroid disease related with Professor Chen were included in the final analysis, involving 144 prescriptions with 190 herbs. The top three high-frequency drugs were Chi Shao (Radix Paeoniae Rubra), Gan Cao (Radix Glycyrrhizae) and Zhe Beimu (Bulbus Fritillariae Thunbergii). Nine pairs of couplet herbs and twenty four groups of 3-herb combinations were respectively obtained. The cluster analysis showed that 8 groups of combinational herbs were obtained, 3 new prescriptions were acquired. Conclusions: Professor Chen's ideology of syndrome differentiation and treatment was flexible and diverse, and innovated on the basis of syndrome differentiation of etiology combination with zang-fu viscera. He treated thyroid diseases with many medicinal forms and took dispersing stagnated liver qi and removing blood stasis as treatment method and has received the good effect. Clinical or Biological Impact: This paper would help related scholars to uphold and deepen the research of inheriting the academic experience based on machine learning, and the effective clinical experience would be provided for young clinicians and even patients on the treatment of thyroid disease.