Knowledge Discovery Method for Treatment Experience on Diabetes Mellitus of Professor LI Sai-mei Based on Theory of Structural Partial-ordered Attribute Diagram
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Knowledge Discovery Method for Treatment Experience on Diabetes Mellitus of Professor LI Sai-mei Based on Theory of Structural Partial-ordered Attribute Diagram
Chinese Journal of Experimental Traditional Medical FormulaeVol. 20, Issue 2, Pages: 207-211(2014)
XU Sun-jing, LI Sai-mei, HONG Wen-xue, et al. Knowledge Discovery Method for Treatment Experience on Diabetes Mellitus of Professor LI Sai-mei Based on Theory of Structural Partial-ordered Attribute Diagram[J]. Chinese journal of experimental traditional medical formulae, 2014, 20(2): 207-211.
DOI:
XU Sun-jing, LI Sai-mei, HONG Wen-xue, et al. Knowledge Discovery Method for Treatment Experience on Diabetes Mellitus of Professor LI Sai-mei Based on Theory of Structural Partial-ordered Attribute Diagram[J]. Chinese journal of experimental traditional medical formulae, 2014, 20(2): 207-211. DOI: 10.11653/syfj2014020207.
Knowledge Discovery Method for Treatment Experience on Diabetes Mellitus of Professor LI Sai-mei Based on Theory of Structural Partial-ordered Attribute Diagram
Objective: To analyze the prescription experience of professor LI Sai-mei for treating diabetes based on the theory of structural partial-ordered attribute diagram. Method: First
we deal with the decision formal context of prescriptions in 200 medical cases about diabetes mellitus in professor LI Sai-mei's clinical working based on principle of the optimization. Then
we generate a new partial-order attribute diagram in order to present the specific c haracter. Finally
we explain properties of partial order structure graph from TCM expert's point of view based on knowledge discovery. Result: Professor Li treated diabetes concentrates on invigorating spleen and kidneys and regulating liver-qi. Conclusion: The method proposed in this paper works well in treating of the description of differentiation of syndromes and discovering new knowledge of clinical diagnosis from the known data.
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