基于古籍文献及名老中医经验的郁病用药规律数据挖掘
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摘要
目的运用数据挖掘技术,整理研究古籍文献及名老中医经验的郁病用药规律。方法建立古籍文献及名老中医郁病诊治数据库,运用IBM SPSS Statistics 21.0及IBM SPSS Modeler软件,使用频数统计、聚类分析、关联规则、因子分析等数据挖掘技术进行分析和比较。结果 (1)辨治用药方面:古籍文献用药更为丰富,重视肝、脾,心、肾脏腑辨证等;因子分析发现,古籍文献更多采用越鞠丸类方、二陈汤类方、归脾丸类方等,而名老中医则更多采用自拟方、经验方。(2)郁病常见兼证配伍药味方面,古籍文献多配伍清热活血,化痰行气之品;名老中医经验则常配伍清热凉血,舒肝养心之品。(3)核心用药及常用药物配伍方面:古籍文献以川芎、香附、茯苓、半夏、陈皮为核心用药,药物配伍有:白芷配桔梗、远志配酸枣仁、沉香配石菖蒲、大黄、郁金、槟榔等;名老中医经验则以柴胡、白芍、当归、茯苓、香附最多,常用药物配伍如:槟榔配佛手、大枣配百合、穿山甲配路路通、皂角刺等。结论对于郁病的诊治,古今用药虽有差别,但无论古籍文献还是当代名老中医经验都具有临证参考价值,不过来源于古籍的用药理论基础可能更为丰富,值得临床进一步挖掘学习。
Objective To investigate the medication rule for depressive disorder based on data mining of ancient traditional Chinese medicine(TCM) books and the experience of famous TCM experts. Methods The database of the diagnosis and treatment of depressive disorder was established based on ancient books and the experience of famous TCM experts. IBM SPSS Statistics 21.0 and IBM SPSS Modeler were used, and data mining techniques including frequency statistics, cluster analysis, association rule, and factor analysis were used for analysis and comparison. Results As for medication for syndrome differentiation and treatment, ancient books had more abundant drugs and emphasized syndrome differentiation based on viscera and bowels such as the liver, the spleen,the heart, and the kidney; factor analysis showed that ancient books tended to use the prescriptions of Yueju Pill, Erchen Decoction, and Guipi Pill, while famous TCM experts tended to use self-made prescriptions and experienced prescriptions. As for drug combination for common accompanying symptoms of depressive disorder, ancient books tended to use heat-clearing, bloodactivating, phlegm-eliminating, and Qi-moving drugs, while famous TCM experts tended to use heat-clearing, blood-cooling, liversoothing, and heart-nourishing drugs. As for core drugs and commonly used drug combinations, ancient books tended to use Rhizoma Ligustici Chuanxiong, Rhizoma Cyperi, Poria cocos, Pinellia ternata, and tangerine peel as core drugs, and commonly used drug combinations included Radix Angelicae Dahuricae combined with Platycodon grandiflorus, Polygala tenuifolia combined with jujube seed, and agilawood combined with Acorus tatarinowii, Rheum officinale, Curcuma aromatica, and areca nut; famous TCM experts tended to use Bupleurum chinense, Radix Paeoniae Alba, Angelica sinensis, Poria cocos, and Rhizoma Cyperi as core drugs, and commonly used drug combinations included areca nut combined with Citrus medica var. sarcodactylis, jujube combined with lily, and pangolin combined with Liquidambaris Fructus and Gleditsiae Spina. Conclusion Although there are differences between ancient and modern medications for the diagnosis and treatment of depressive disorder, both ancient books and the experience of famous TCM experts have a reference value in clinical practice. There are abundant medication theories derived from ancient books, which is worthy of further data mining and research.
Objective To investigate the medication rule for depressive disorder based on data mining of ancient traditional Chinese medicine(TCM) books and the experience of famous TCM experts. Methods The database of the diagnosis and treatment of depressive disorder was established based on ancient books and the experience of famous TCM experts. IBM SPSS Statistics 21.0 and IBM SPSS Modeler were used, and data mining techniques including frequency statistics, cluster analysis, association rule, and factor analysis were used for analysis and comparison. Results As for medication for syndrome differentiation and treatment, ancient books had more abundant drugs and emphasized syndrome differentiation based on viscera and bowels such as the liver, the spleen,the heart, and the kidney; factor analysis showed that ancient books tended to use the prescriptions of Yueju Pill, Erchen Decoction, and Guipi Pill, while famous TCM experts tended to use self-made prescriptions and experienced prescriptions. As for drug combination for common accompanying symptoms of depressive disorder, ancient books tended to use heat-clearing, bloodactivating, phlegm-eliminating, and Qi-moving drugs, while famous TCM experts tended to use heat-clearing, blood-cooling, liversoothing, and heart-nourishing drugs. As for core drugs and commonly used drug combinations, ancient books tended to use Rhizoma Ligustici Chuanxiong, Rhizoma Cyperi, Poria cocos, Pinellia ternata, and tangerine peel as core drugs, and commonly used drug combinations included Radix Angelicae Dahuricae combined with Platycodon grandiflorus, Polygala tenuifolia combined with jujube seed, and agilawood combined with Acorus tatarinowii, Rheum officinale, Curcuma aromatica, and areca nut; famous TCM experts tended to use Bupleurum chinense, Radix Paeoniae Alba, Angelica sinensis, Poria cocos, and Rhizoma Cyperi as core drugs, and commonly used drug combinations included areca nut combined with Citrus medica var. sarcodactylis, jujube combined with lily, and pangolin combined with Liquidambaris Fructus and Gleditsiae Spina. Conclusion Although there are differences between ancient and modern medications for the diagnosis and treatment of depressive disorder, both ancient books and the experience of famous TCM experts have a reference value in clinical practice. There are abundant medication theories derived from ancient books, which is worthy of further data mining and research.
引文
[1]鱼浚镛,田金洲.试述中医对抑郁症的认识[J].天津中医药,2011,28(4):343-345.
[2]何玉梅,杨东东.郁证的近代各家经验概述[J].中医药临床杂志,2016,28(2):256-258.
[3]王亮,隋南,王力,等.心理健康问题基础研究和干预技术进展[J].中国科学院院刊,2016,31(11):1171-1186.
[4] PRICE J B, BRONARS C, ERHARDT S, et al. Bioenergetics and synaptic plasticity as potential targets For individualizing treatment for depression[J]. Neuroscience&Biobehavioral Reviews,2018,90(7):212-220.
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[6] VALD魪STOVAR M, ESTRADAREYES R, SOL魱SCHAGOY魣N H, et al. Circadian modulation of neuroplasticity by melatonin:A target in the treatment of depression[J]. British Journal of Pharmacology, 2018,175(16):3200-3208.
[7]安春平,吴海燕,胡妮娜,等.基于GoPubMed针灸治疗抑郁症的文献计量学分析[J].针灸临床杂志,2017,33(4):54-58.
[8]吴晓霞,陈广,蔡虎志,等.陈新宇教授论治郁证经验举隅[J].湖南中医药大学学报,2018,38(10):1146-1148.
[9]王旭东,乔明琦,张樟进,等.中医药治疗抑郁症的研究进展[J].南京中医药大学学报,2016,32(1):93-96.
[10]李良娥,谭文娟,秦俭,等.耳穴按压对中晚期恶性肿瘤患者负性情绪的干预研究[J].湖南中医药大学学报,2016,36(7):68-70.
[11]樊志明,连曄曄,连建伟.连建伟教授辨治郁证六法[J].湖南中医药大学学报,2017,37(8):861-862.
[12]袁春云,袁思斯.艾斯西酞普兰联合归脾汤治疗老年躯体形式障碍的临床观察[J].湖南中医药大学学报,2018,38(8):921-924.
[13]王琼,曹徵良,孙佳琪,等.湖湘针推学术流派“五经配伍”治疗郁病浅析[J].湖南中医药大学学报,2015,35(8):10-12.
[14]赵艳青,项敏泓,王雪菲,等.基于因子分析与关联规则挖掘干眼症的用药规律[J].中华中医药杂志,2017,32(10):4708-4712.
[15]赵艳青,李青松,项敏泓,等.中医药数据挖掘中常见问题的思考与策略———以中医药术语及用药规律总结为例[J].中华中医药杂志,2018,33(4):1220-1225.
[16]周志焕,高树明,高杉,等.基于关联规则的郁证古方用药规律分析[J].天津中医药,2015,32(3):176-179.
[17]饶克勤.卫生统计方法与应用进展[M].北京:人民卫生出版社,2008:284.
[2]何玉梅,杨东东.郁证的近代各家经验概述[J].中医药临床杂志,2016,28(2):256-258.
[3]王亮,隋南,王力,等.心理健康问题基础研究和干预技术进展[J].中国科学院院刊,2016,31(11):1171-1186.
[4] PRICE J B, BRONARS C, ERHARDT S, et al. Bioenergetics and synaptic plasticity as potential targets For individualizing treatment for depression[J]. Neuroscience&Biobehavioral Reviews,2018,90(7):212-220.
[5] BAYES A J, PARKER G B. Comparison of guidelines for the treatment of unipolar depression:a focus on pharmacotherapy and neurostimulation[J]. Acta Psychiatrica Scandinavica, 2018,137(6):459-471.
[6] VALD魪STOVAR M, ESTRADAREYES R, SOL魱SCHAGOY魣N H, et al. Circadian modulation of neuroplasticity by melatonin:A target in the treatment of depression[J]. British Journal of Pharmacology, 2018,175(16):3200-3208.
[7]安春平,吴海燕,胡妮娜,等.基于GoPubMed针灸治疗抑郁症的文献计量学分析[J].针灸临床杂志,2017,33(4):54-58.
[8]吴晓霞,陈广,蔡虎志,等.陈新宇教授论治郁证经验举隅[J].湖南中医药大学学报,2018,38(10):1146-1148.
[9]王旭东,乔明琦,张樟进,等.中医药治疗抑郁症的研究进展[J].南京中医药大学学报,2016,32(1):93-96.
[10]李良娥,谭文娟,秦俭,等.耳穴按压对中晚期恶性肿瘤患者负性情绪的干预研究[J].湖南中医药大学学报,2016,36(7):68-70.
[11]樊志明,连曄曄,连建伟.连建伟教授辨治郁证六法[J].湖南中医药大学学报,2017,37(8):861-862.
[12]袁春云,袁思斯.艾斯西酞普兰联合归脾汤治疗老年躯体形式障碍的临床观察[J].湖南中医药大学学报,2018,38(8):921-924.
[13]王琼,曹徵良,孙佳琪,等.湖湘针推学术流派“五经配伍”治疗郁病浅析[J].湖南中医药大学学报,2015,35(8):10-12.
[14]赵艳青,项敏泓,王雪菲,等.基于因子分析与关联规则挖掘干眼症的用药规律[J].中华中医药杂志,2017,32(10):4708-4712.
[15]赵艳青,李青松,项敏泓,等.中医药数据挖掘中常见问题的思考与策略———以中医药术语及用药规律总结为例[J].中华中医药杂志,2018,33(4):1220-1225.
[16]周志焕,高树明,高杉,等.基于关联规则的郁证古方用药规律分析[J].天津中医药,2015,32(3):176-179.
[17]饶克勤.卫生统计方法与应用进展[M].北京:人民卫生出版社,2008:284.