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Mining Symptom-Herb Patterns from Patient Records Using Tripartite Graph.

Jinpeng Chen1, Josiah Poon2, Simon K Poon2

  • 1School of Computer Science and Engineering, BeiHang University, Beijing, China.

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Summary
This summary is machine-generated.

Traditional Chinese Medicine (TCM) uses syndrome differentiation (SD) for patient classification. This study introduces a novel method to uncover symptom-herb patterns, improving personalized TCM treatment efficacy.

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Area of Science:

  • Computational medicine
  • Bioinformatics
  • Traditional Chinese Medicine (TCM)

Background:

  • TCM employs syndrome differentiation (SD) for patient classification, a unique step before personalized treatment.
  • Identifying patterns between symptoms, syndromes, and herbs is crucial for effective TCM prescriptions.

Purpose of the Study:

  • To propose a novel three-step mining approach to uncover the triangular relationship between symptoms, syndromes, and herbs in TCM.
  • To enhance the efficacy of TCM treatments through improved personalization.

Main Methods:

  • Construction of a heterogeneous tripartite information network to represent complex relationships.
  • Systematic extraction of path-based topological features from the network.
  • Application of an unsupervised method to learn parameters for symptom-herb relationship determination.

Main Results:

  • The proposed method successfully identified patterns between symptoms and herbs within the TCM framework.
  • Experiments on real-world data for Insomnia, Diabetes, Infertility, and Tourette syndrome yielded insightful results.
  • The approach highlights the intricate connections within TCM diagnosis and treatment.

Conclusions:

  • The novel three-step mining approach effectively models the symptom-syndrome-herb relationship in TCM.
  • This method offers a promising direction for personalized medicine and improving treatment outcomes in TCM.
  • Further research can leverage this approach for broader applications in traditional medicine systems.