Volume 37 Issue 5
Sep.  2021
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Article Navigation >  Journal of Nanjing University of traditional Chinese Medicine  > 2021  >  37(5): 760-764
SHEN Gang-lei, JIN Chun-hui, NI Yi-qun, YANG Tao, XIE Jia-dong, HU Kong-fa. Comparative Study on the Medication Characteristics of Well-Known Veteran Traditional Chinese Medicine Doctors in the Treatment of Lung Cancer Based on Complex Networks Algorithm[J]. Journal of Nanjing University of traditional Chinese Medicine, 2021, 37(5): 760-764. doi: 10.14148/j.issn.1672-0482.2021.0760
Citation: SHEN Gang-lei, JIN Chun-hui, NI Yi-qun, YANG Tao, XIE Jia-dong, HU Kong-fa. Comparative Study on the Medication Characteristics of Well-Known Veteran Traditional Chinese Medicine Doctors in the Treatment of Lung Cancer Based on Complex Networks Algorithm[J]. Journal of Nanjing University of traditional Chinese Medicine, 2021, 37(5): 760-764. doi: 10.14148/j.issn.1672-0482.2021.0760
shu

Comparative Study on the Medication Characteristics of Well-Known Veteran Traditional Chinese Medicine Doctors in the Treatment of Lung Cancer Based on Complex Networks Algorithm

doi: 10.14148/j.issn.1672-0482.2021.0760
  • 1.

    School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China

  • 2.

    Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, 214071, China

  • 3.

    Inheritance Studio of Well-known Veteran TCM Expert Zhao Jingfang, Wuxi TCM Hospital, Wuxi, 214071, China

  • Received Date: 2021-03-02
    Available Online: 2021-12-21
  • Publish Date: 2021-09-10
    Abstract
  •   OBJECTIVE   To compare the characteristics of medication based on syndrome differentiation in the treatment of lung cancer between Professor Zhou Zhongying (Professor Zhou) and Professor Zhao Jingfang (Professor Zhao) by complex network algorithm, and to provide a new research idea and data for summarizing and inheriting the clinical experience of well-known veteran traditional Chinese medicine (TCM) doctors.   METHODS   The lung cancer outpatient electronic medical records diagnosed by Professor Zhou and Professor Zhao were collected, and the complex network algorithm was applied to construct "syndrome-syndrome" and "syndrome-medication" complex network maps based on the two doctors' medical records, so as to analyze the individualized differences between their experience in medication based on syndrome differentiation of lung cancer.   RESULTS   Through the analysis of the complex network map, it was found that the syndromes of lung cancer identified by the two doctors both included deficiency of qi and yin. As for the differences, Professor Zhou identified syndromes as phlegm stagnation and lung constraint, heat toxin and phlegm stagnation, spreading of cancerous toxin, as well as binding of phlegm and stasis, while Professor Zhao identified syndromes as deficiency of lung and spleen qi, phlegm-heat accumulation in lung, spleen deficiency and phlegm dampness, along with liver constraint and spleen deficiency. The medications commonly used by Professor Zhou in treating lung cancer included Glehniae Radix, Adenophorae Radix, Ophiopogonis Radix, Cremastrae Pseudobulbus Pleiones Pseudobulbus, Herba Euphorbiae Helioscopiae, Pseudostellariae Radix, Agrimoniae Herba, Ranunculi Ternati Radix, Hedyotis diffusa Willd, and processed Bombyx Batryticatus, while Professor Zhao commonly used Codonopsis Radix, stir-fried Atractylodis Macrocephalae Rhizoma, processed Pinelliae Rhizoma, Poria, Pseudostellariae Radix, Adenophorae Radix, Glehniae Radix, Citri Reticulatae Pericarpium, Coicis Semen, Polyporus, Setariae Fructus Germinatus, Hordei Fructus Germinatus, and honeyed Eriobotryae Folium.   CONCLUSION   Treatment of lung cancer from Professor Zhou is based on the cancerous toxin theory, and he emphasizes treatment based on syndrome differentiation of cancerous toxin, while treatment of lung cancer from Professor Zhao is based on the theory of maintaining balance with the moderate regulation, and her main treatment method is banking up earth to generate metal.

     

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