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The Causality Research between Syndrome Elements by Attribute Topology.

Tao Zhang1,2, Mengqi Liu1,2, Wenyuan Liu1

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Area of Science:

  • Computational Medicine
  • Traditional Chinese Medicine Research
  • Data Mining

Background:

  • Traditional Chinese Medicine (TCM) diagnosis relies on empirical observations and syndrome elements.
  • Existing research on TCM syndrome elements primarily examines correlations and hierarchies, neglecting causality.
  • Causality between TCM syndrome elements remains an under-explored area.

Purpose of the Study:

  • To propose and validate a novel method for exploring causality among TCM syndrome elements.
  • To establish a foundation for knowledge discovery in TCM diagnostic data.

Main Methods:

  • A new method, causality by attribute topology (CAT), was developed based on attribute topology subordinate relations.
  • The CAT method infers dependency relationships by analyzing and updating attribute sets within a formal context.
  • 500 TCM records were mathematically transformed into a binary context for syndrome element analysis.

Main Results:

  • The study successfully verified causal transformations between TCM syndrome elements using the CAT method.
  • Experimental results indicated distinct syndrome element characteristics and relationships between male and female groups.
  • Findings were largely consistent with established TCM theory.

Conclusions:

  • Causality by attribute topology (CAT) is a feasible approach for describing causal relationships between TCM syndrome elements.
  • Further research is warranted to explore knowledge discovery in TCM diagnostic data through causal analysis.
  • The CAT method provides a new avenue for understanding the complex interactions within TCM diagnostics.