[Mutual information-based correlation analysis of herbs against insomnia]

Area of Science:

• Traditional Chinese Medicine (TCM)
• Computational Data Mining
• Herbal Pharmacology

Context:

• Insomnia is a prevalent sleep disorder with complex etiologies.
• Professor Guo Rongjuan's extensive clinical experience in TCM insomnia therapy provides a valuable dataset.
• The TCM Inheritance Support Platform facilitates the analysis of complex clinical data.

Purpose:

• To analyze Professor Guo Rongjuan's medication experience for insomnia therapy using data mining techniques.
• To identify core herbal combinations and association rules in TCM insomnia treatment.
• To validate the identified patterns against Professor Guo's established TCM theories.

Summary:

• Data mining algorithms (Apriori, modified mutual-information, entropy clustering) were applied to 3,084 TCM formulae prescribed for insomnia.
• Analysis revealed herb frequencies, identified 41 core combinations, and generated 7 novel formulae.
• The findings support Professor Guo's theories on insomnia pathogenesis, including "pathogenic fire derived from stagnation of liver-QI" and "liver stagnation and spleen deficiency."

Impact:

• Provides new insights into Professor Guo's effective TCM insomnia treatment strategies.
• Demonstrates the practical value of the TCM Inheritance Support Platform for mining clinical expertise.
• Offers potential for developing evidence-based TCM interventions for insomnia.