HE Yaqi, DENG Yinhe, MO Jiahao, CHEN Jie, YANG Zhongqi, XIAN Shaoxiang. Medication Rules of Traditional Chinese Medicine Patent Compound Prescriptions in Treating Insomnia in 2009-2019[J]. Chinese Journal of Modern Applied Pharmacy, 2020, 37(16): 1926-1932. DOI: 10.13748/j.cnki.issn1007-7693.2020.16.002
    Citation: HE Yaqi, DENG Yinhe, MO Jiahao, CHEN Jie, YANG Zhongqi, XIAN Shaoxiang. Medication Rules of Traditional Chinese Medicine Patent Compound Prescriptions in Treating Insomnia in 2009-2019[J]. Chinese Journal of Modern Applied Pharmacy, 2020, 37(16): 1926-1932. DOI: 10.13748/j.cnki.issn1007-7693.2020.16.002

    Medication Rules of Traditional Chinese Medicine Patent Compound Prescriptions in Treating Insomnia in 2009-2019

    • OBJECTIVE To analyze the medication rule of traditional Chinese medicine compound prescription for insomnia. METHODS The Chinese medicine compound prescriptions for insomnia were retrieved by searching Patent Search and Analysis of SIPO. The Ancient and Modern Medical Records Cloud Platform(V1.5.7), SPSS Clementine 12.0 software and SPSS 22.0 software were used respectively for the frequency statistics, four nature and five flavors of traditional Chinese medicine, meridian attribution statistics, correlation analysis, cluster analysis and principal component analysis to explore the medication rules of patent compound prescriptions for insomnia. RESULTS A total of 528 Chinese medicine compound prescriptions were screened and 327 Chinese medicines were obtained with an average of 12.77 Chinese medicines per compound prescription. Thirty high frequency Chinese medicines were obtained, and the most commonly used drugs were nourishing heart and calming mind drugs, Qi-tonifying drugs and Blood-tonifying drugs. The medication of four nature was mainly calm, warm and slightly cold, while the five flavors were mainly sweet, bitter and pungent, and the meridians of heart and liver were the most common. A total of 76 items were obtained by association rule analysis. Including 52 pairs of medicine, 20 and 4 pairs of medicine of three and four groups respectively. Nine medicine groups were obtained by cluster analysis, and 12 principal components with eigenvalue >1 were obtained by principal component analysis. CONCLUSION The results obtained are valuable and innovative, but there are some limitations due to the quality of patents.
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