从病症结合探讨治疗中风后半身不遂古方的用药规律

Administration Rules in Ancient Prescriptions for the treatment of Hemiplegia After Stroke from the Perspective of Integrated Disease and Syndrome

目的:采用频数分析和关联分析法,从病症结合探寻中风后半身不遂古方的用药规律。方法:选取中华医典5.0系统中治疗中风后半身不遂的方剂,利用Excel软件建成"中风后半身不遂古方用药"数据库,并用SPSS Modeler18.0软件对数据库进行频数分析和关联分析。结果:古方治疗中风后半身不遂常用药物有防风、当归、川芎、肉桂、附子、麻黄、人参、羌活、天麻、独活等。最常见的药类为补虚药、解表药、活血化瘀药、祛风湿药、温里药。最常用的药对为川芎-防风、麻黄-防风、羌活-防风、人参-防风、独活-防风。最常用的药组为防风-麻黄-川芎、川芎-麻黄-防风、当归-肉桂-防风。结论:数据挖掘法能够较全面地阐释古代医家组方用药经验,对现代遣方用药有借鉴意义。

Objective： To apply the methods of frequency analysis and correlation analysis to explore the rule of the diagnosis and treatment in perspective of intergrated disease and syndrome for hemiplegia after stroke. Methods： Selected the prescriptions for hemiplegia after stroke in the Chinese Medical Book System 5.0, established the database of herbal medicines used in ancient prescriptions for hemiplegia after stroke with Excel, and conducted frequency analysis and correlation analysis on the database with SPSS Modeler18.0. Results： The commonly used herbal medicines to treat hemiplegia after stroke include Radix Saposhnikoviae, Radix Angelicae Sinensis, Rhizoma Chuanxiong, Cortex Cinnamomi, Radix Aconiti Lateralis Praeparata, Herba Ephedrae, Radix et Rhizoma Ginseng, Rhizoma et Radix Notopterygii, Rhizoma Gastrodiae, Radix Angelicae Pubescentis, etc. The most common medicine types are deficiency-supplementing medicine, exterior-releasing medicine, blood-invigorating and stasis-dissolving medicine, wind-dampness-dispelling medicine, and the interior- warming medicine. The most common couplet medicines are Rhizoma Chuanxiong-Radix Saposhnikoviae, Herba Ephedrae-Radix Saposhnikoviae, Rhizoma et Radix Notopterygii-Radix Saposhnikoviae, Radix et Rhizoma Ginseng-Radix Saposhnikoviae, and Radix Angelicae Pubescentis-Radix Saposhnikoviae. The most common medicine groups are Radix Saposhnikoviae-Herba Ephedrae-Rhizoma Chuanxiong, Rhizoma Chuanxiong-Herba Ephedrae-Radix Saposhnikoviae, and Radix Angelicae Sinensis-Cortex Cinnamomi-Radix Saposhnikoviae. Conclusion. The data mining method can fully explain the administration experience of ancient physicians in composing prescriptions, and can be used as a reference for modern prescriptions.