摘要
目的:通过增加中医处方药物的剂量特征改进关联规则算法以探寻处方规律。方法:将处方药物的平均剂量以及配伍减毒增效关系纳入关联规则算法改进模型中,分别构建不同加权支持度,用Python实现改进后的算法,并对结果进行可视化,最后在中医临床专家的指导下论证模型的有效性。结果:根据2种加权方法设计了2种改进挖掘模型,在相同支持度下,2种改进模型均获得了比传统模型更多的药物组合,其中配伍减毒增效加权模型效果更为显著。结论:使用本文提出的改进挖掘模型可以挖掘到更多频繁项集,减少用药规律的遗漏,对中医处方规律研究有较强实用价值。
Objective: By improving the existing association rules algorithm through increasing the dose characteristics of the prescription drugs of traditional Chinese medicine(TCM) to find out the rules of the prescription better. Methods:First, the dose-weighted support was constructed based on the relationship between the average dose of prescription drugs and the drug-attenuated synergistic effect. Then, based on different support degrees, the improved association rule models were implemented using Python and visualize the results. Finally, the validity of the model was demonstrated under the guidance of TCM clinical experts. Results: Two kinds of improved mining models based on association rules were constructed respectively based on two kinds of weighting methods. Under the same support degree, two kinds of improved models obtained more drug combinations than the traditional model, and the second new model is more significant. Conclusion: By using the improved mining model proposed in this paper, more frequent itemsets can be mined and fewer medication rules will be missed, which has a strong practical value for the study of the rule of TCM prescription.
引文
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