Digital Chinese Medicine (Jun 2022)
Traditional Chinese medicine Master XIONG Jibo’s medication experience in treating arthralgia syndrome through data mining
Abstract
Objective: This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine (TCM) Master XIONG Jibo in diagnosing and treating arthralgia syndrome (AS) through data mining. Methods: Data of outpatient cases of Professor XIONG Jibo were collected from January 1, 2014 to December 31, 2018, along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor: XIONG Jibo's Clinical Medical Record 1, which was published in December 2019. The five variables collected from the patients’ data were TCM diagnostic information, TCM and western medicine diagnoses, syndrome, treatment, and prescription. A database was established for the collected data with Excel. Using the Python environment, a customized modified natural language processing (NLP) model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud. Frequency analysis, association rule analysis, cluster analysis, and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform (V3.0) and RStudio (V4.0.3). Results: A total of 610 medical records of Professor XIONG Jibo were collected from the case database. A total of 103 medical records were included after data screening criteria, which comprised 187 times (45 kinds) of prescriptions and 1 506 times (125 kinds) of Chinese herbs. The main related meridians were the liver, spleen, and kidney meridians. The properties of Chinese herbs used most were mainly warm, flat, and cold, while the flavors of herbs were mainly bitter, pungent, and sweet. The main patterns of AS included the damp heat, phlegm stasis, and neck arthralgia. The most commonly used herbs for AS were Chuanniuxi (Cyathulae Radix), Huangbo (Phellodendri Chinensis Cortex), Cangzhu (Atractylodis Rhizoma), Qinjiao (Gentianae Macrophyllae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangqi (Astragali Radix), and Chuanxiong (Chuanxiong Rhizoma). The most common effect of the herbs was “promoting blood circulation and removing blood stasis”, followed by “supplementing deficiency (Qi supplementing, blood supplementing, and Yang supplementing)”, and “dispelling wind and dampness”. The data were analyzed with the support ≥ 15% and confidence = 100%, and after de-duplication, five second-order association rules, 39 third-order association rules, 39 fourth-order association rules, and two fifth-order association rules were identified. The top-ranking association rules of each were “Cangzhu (Atractylodis Rhizoma) → Huangbo (Phellodendri Chinensis Cortex)” “Cangzhu (Atractylodis Rhizoma) + Chuanniuxi (Cyathulae Radix) → Huangbo (Phellodendri Chinensis Cortex)” “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) + Gancao (Glycyrrhizae Radix et Rhizoma) → Qinjiao (Gentianae Macrophyllae Radix)” and “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) +Gancao (Glycyrrhizae Radix et Rhizoma) + Huangbo (Phellodendri Chinensis Cortex) → Qinjiao (Gentianae Macrophyllae Radix)”, respectively. Five clusters were obtained using cluster analysis of the top 30 herbs. The herbs were mainly drying dampness, supplementing Qi, and promoting blood circulation. The main prescriptions of AS were Ermiao San (二妙散), Gegen Jianghuang San (葛根姜黄散), and Huangqi Chongteng Yin (黄芪虫藤饮). The herbs of core prescription included Cangzhu (Atractylodis Rhizoma), Chuanniuxi (Cyathulae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangbo (Phellodendri Chinensis Cortex), Mugua (Chaenomelis Fructus), Qinjiao (Gentianae Macrophyllae Radix), Danggui (Angelicae Sinensis Radix), and Yiyiren (Coicis Semen). Conclusion: Clearing heat and dampness, relieving collaterals and pain, and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo. Additionally, customized NLP model could improve the efficiency of data mining in TCM.
