基于循证的中药药性判别数据挖掘方法评价
|
摘要
目的:通过循证方法分析评价数据挖掘方法在中药性效中的应用效果。方法:通过检索万方、知网、维普数据库中的相关文献,按研究需求对文献进行筛选,并使用STATA 15.0对提取的资料进行分析。结果:共搜集文献2 064篇,最终纳入有效文献3篇,含6种数据挖掘方法,共进行8组比较。其中"支持向量机"和"回归判别分析"预测效果比较,总预测准确率为(OR=1.74,95%CI [1.13,2.68]);其中两组比较存在异质性;剩余5组无统计学意义。结论:"支持向量机"在中药药性判别中的预测效果优于"回归判别分析";其他比较组的方法的比较结果均无明显优势,需更多高质量的研究证实。
Objective: To analyze the application effect of data mining methods in the efficacy of traditional Chinese medicine through evidence-based methods. Methods: By searching relevant literatures in Wanfang, CNKI and VIP databases,the literature was screened according to research needs, and the extracted data were analyzed by STATA 15.0. Results: A total of2 064 articles were collected, and 3 valid articles were finally included, including 6 data mining methods. A total of 8 groups were compared. Among them, ‘SVM' and ‘Logistic-DA' predictive effect comparison, the total prediction accuracy rate(OR=1.74,95%CI[1.13, 2.68]); two groups are heterogeneous; the rest 5 groups are not statistical significance. Conclusion: The prediction effect of ‘SVM' in the judgment of traditional Chinese medicine is better than ‘Logistic-DA'. There is no obvious advantage in the mothed of other comparative groups, and more high-quality studies are needed to confirm it.
Objective: To analyze the application effect of data mining methods in the efficacy of traditional Chinese medicine through evidence-based methods. Methods: By searching relevant literatures in Wanfang, CNKI and VIP databases,the literature was screened according to research needs, and the extracted data were analyzed by STATA 15.0. Results: A total of2 064 articles were collected, and 3 valid articles were finally included, including 6 data mining methods. A total of 8 groups were compared. Among them, ‘SVM' and ‘Logistic-DA' predictive effect comparison, the total prediction accuracy rate(OR=1.74,95%CI[1.13, 2.68]); two groups are heterogeneous; the rest 5 groups are not statistical significance. Conclusion: The prediction effect of ‘SVM' in the judgment of traditional Chinese medicine is better than ‘Logistic-DA'. There is no obvious advantage in the mothed of other comparative groups, and more high-quality studies are needed to confirm it.
引文
[1]王晓燕,李峰.60种中药脂类成分GC-MS图谱与寒热药性关系的统计识别模式研究.辽宁中医杂志,2015,42(6):1303-1305
[2]王鹏,周洪雷,薛付忠,等.60种植物类中药提取物的红外光谱分析及其与寒热药性相关性的模式识别评价研究.光谱学与光谱分析,2014,34(1):58-63
[3]龙伟.“计算中药学”在中药药性及复方研究中的应用.北京:北京协和医学院,2011
[4]么鸿雁,施侣元.中国人群肺癌发病危险因素Meta分析.中华流行病学杂志,2003,24(1):45-49
[5]张培,李江,王耘,等.贝叶斯网络在中药有效组分五味预测中的应用.世界科学技术:中医药现代化,2008,10(5):114-117
[6]周密,王耘,乔延江.缺失药性补遗与完备的方法学研究.世界科学技术:中医药现代化,2008,10(5):118-120
[7]叶铮.中药的性味归经与降脂药效的BP神经网络研究.广州:华南师范大学,2008
[8]刘永琦,陈丽,范萍,等.中药特征与神经效应的关系分析及其Logistic回归模型.中国中药杂志,2009,34(3):251-254
[9]李艳菊,刘辉,王虹菲,等.基于文献数据库分析中药特征与免疫效应的关系及其数学模型的建立.中国中药杂志,2007,32(17):1783-1786
[10]王鹏,王振国,薛付忠,等.基于支持向量机法的中药性状与药性相关性研究.江西中医药,2012,43(7):65-68
[11]赵兴业.中药寒热药性生理生化评价指标的初步研究.北京:北京中医药大学,2007
[12]吴思媛,胡幼芬,刘晓伟,等.中药寒热药性分类的随机森林模型研究.软件导刊,2014,13(10):71-74
[13]胡亚楠,王梅,曹佳,等.组分中药药性预测平台构建.中华中医药杂志,2016,31(3):965-967
[2]王鹏,周洪雷,薛付忠,等.60种植物类中药提取物的红外光谱分析及其与寒热药性相关性的模式识别评价研究.光谱学与光谱分析,2014,34(1):58-63
[3]龙伟.“计算中药学”在中药药性及复方研究中的应用.北京:北京协和医学院,2011
[4]么鸿雁,施侣元.中国人群肺癌发病危险因素Meta分析.中华流行病学杂志,2003,24(1):45-49
[5]张培,李江,王耘,等.贝叶斯网络在中药有效组分五味预测中的应用.世界科学技术:中医药现代化,2008,10(5):114-117
[6]周密,王耘,乔延江.缺失药性补遗与完备的方法学研究.世界科学技术:中医药现代化,2008,10(5):118-120
[7]叶铮.中药的性味归经与降脂药效的BP神经网络研究.广州:华南师范大学,2008
[8]刘永琦,陈丽,范萍,等.中药特征与神经效应的关系分析及其Logistic回归模型.中国中药杂志,2009,34(3):251-254
[9]李艳菊,刘辉,王虹菲,等.基于文献数据库分析中药特征与免疫效应的关系及其数学模型的建立.中国中药杂志,2007,32(17):1783-1786
[10]王鹏,王振国,薛付忠,等.基于支持向量机法的中药性状与药性相关性研究.江西中医药,2012,43(7):65-68
[11]赵兴业.中药寒热药性生理生化评价指标的初步研究.北京:北京中医药大学,2007
[12]吴思媛,胡幼芬,刘晓伟,等.中药寒热药性分类的随机森林模型研究.软件导刊,2014,13(10):71-74
[13]胡亚楠,王梅,曹佳,等.组分中药药性预测平台构建.中华中医药杂志,2016,31(3):965-967