基于Apriori改进算法的名老中医肺癌医案用药规律研究

Study on Law of Drug Use of Famous TCM Physicians Treating Patients with Lung Cancer Based on Apriori Improved Algorithm

目的:改进Apriori算法以适应名老中医诊疗方案的数据的一药多剂量特点,挖掘名老中医对肺癌医案的用药规律。方法:提出一种增加了信息熵作为权重的ACMC算法,对名老中医肺癌医案的用药规律、用药剂量进行数据挖掘。结果:选择通过审核的215个药方,1806味药物,找出关联规则153条,发现有的药物不仅被频繁使用,而且不同剂量的药物也被频繁的使用,说明该药物在治疗肺癌中被名老中医认为是非常重要。结论:基于apriori算法的ACMC算法,不仅仅能挖掘出用药规律,而且增加了信息熵作为权重,可以更好适用于挖掘那些隐藏的用药规律。

Objective:To improve the Apriori algorithm to adapt the data of the old Chinese medicine diagnosis and treatment program of multiple doses of the same drug,mining the lung cancer medicine law.Methods:A new ACMC algorithm was proposed to increase the information entropy as the weight,and the data was used to excavate the drug law and dosage of lung cancer.Results:Among the 215 prescriptions,1806 kinds of drugs had 153 association rules.It is found that some drugs are not only frequently used and different doses of drugs are frequently used,indicating that the drug in the treatment of lung cancer is very important.Conclusion:The ACMC algorithm based on Apriori algorithm can not only excavate the law of drug use,but also increase the information entropy as the weight,which can be applied to the mining of hidden drug laws.