[关键词]
[摘要]
目的 基于数据挖掘方法研究治疗目赤肿痛、雀目、胬肉攀睛、睑弦赤烂和翳障5种眼科疾病方剂的组方用药规律。方法 从《中医方剂大辞典》中收集和整理治疗5种眼科疾病的方剂共1 999首,以Apriori关联规则算法为基础,以支持度、置信度、提升度为指标进行数据挖掘,并进行频数分析及关联规则分析。结果 治疗目赤肿痛、胬肉攀睛、睑弦赤烂的常用中药类别为清热药和解表药等,治疗雀目、翳障的常用中药类别为清热药和补虚药等。目赤肿痛方剂中黄连使用频率较高,睑弦赤烂中荆芥使用频率较高,翳障中川芎使用频率较高。治疗目赤肿痛的潜在关联规则为珍珠→炉甘石等,胬肉攀睛中为没药→乳香等,睑弦赤烂中为羌活→防风等,翳障中为密陀僧→硇砂等。结论 运用关联规则对比研究5种眼科疾病用药,可反映各眼科病的中药治疗手段异同,发掘组方用药规律,从而挖掘中药方剂的潜在价值。
[Key word]
[Abstract]
Objective To analyze the composition regularities of the traditional Chinese medicine formulas (TCMFs) for treating five kinds of ophthalmic diseases, including swelling and pain of eyes, night blindness, pterygium, blepharitis marginalis and nebula based on the data mining methodology. Methods A total of 1999 TCMFs for ophthalmic diseases were collected from the Dictionary of Chinese Medicine Prescriptions. Then frequency analyses and association rules analyses were conducted with the three indicators of support, confidence and lift based on the Apriori algorithm. Results The diaphoretic drugs and heat-clearing drugs were the commonly used types of the traditional Chinese medicines (TCMs) for swelling and pain of eyes, pterygium and blepharitis marginalis. The heat-clearing drugs and tonifying drugs were the commonly used types of TCMs for night blindness and nebula. Coptidis Rhizoma was frequently used for swelling and pain of eyes, Schizonepetae Herba was for blepharitis marginalis, and Chuanxiong Rhizoma was for nebula. The latent association rules with significant lift included Margarita→Calamina for swelling and pain of eyes, Myrrha→Olibanum for pterygium, Notopterygii Rhizoma et Radix→Saposhnikoviae Radix for blepharitis marginalis, and Lithargite→Sal Ammoniac for nebula. Conclusion Using association rules, the comparative study on the five kinds of ophthalmic diseases can reveal the similarities and differences of treatments for these ophthalmic diseases and explore the composition regularities of TCMs, which helps to explore the hidden value of TCMFs.
[中图分类号]
R28;R287.81
[基金项目]
国家自然科学基金资助项目(81703462);浙江省基础公益研究计划项目(LGF19H280005);中国博士后科学基金面上项目(2018M642495)