灰色马尔可夫模型在中医证候预测中的应用进展
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摘要
多学科交叉融合以及多种统计学方法的广泛应用,为中医证候的客观化研究提供了许多行之有效的方法。通过梳理近几年文献发现,目前中医证候研究存在资料的采集、证候的判定和证候量化方面主观性较大,针对临床证候的不平衡性及动态性研究相对较少、缺乏综合可行的研究方法等诸多问题。围绕中医证候的兼夹性、动态性、不平衡性、复杂性等特点,文章将灰色模型和马尔可夫模型相结合,探讨灰色马尔可夫模型在中医证候预测中运用的科学性,以期提高中医辨证论治的准确性、科学性,进而提高临床诊疗效果,推进中医证候研究的客观化进程。
Multidisciplinary cross-integration and a wide range of statistical methods widely used to provide many effective methods for the objective study of TCM syndromes.Through sorting the literature published in recent years,there are many problems have been found,such as the strong subjectivity in collecting data,judging and quantifying the syndromes of TCM.Also,the researches aiming at studying the imbalance and dynamic of clinical syndromes are negative as well as lacking comprehensive and feasible research methods,etc.In order to improve the accuracy and scientificity of TCM differentiation and treatment,we consequently improved the efficiency of clinical diagnosis and treatment and promoted the objective process of TCM syndromes.This paper integrates grey model and Markov model to discuss the scientific application of Grey Markov model in TCM syndrome prediction,centering on the characteristics of syndromes of TCM,such as inclusion,dynamics,imbalance and complexity.
Multidisciplinary cross-integration and a wide range of statistical methods widely used to provide many effective methods for the objective study of TCM syndromes.Through sorting the literature published in recent years,there are many problems have been found,such as the strong subjectivity in collecting data,judging and quantifying the syndromes of TCM.Also,the researches aiming at studying the imbalance and dynamic of clinical syndromes are negative as well as lacking comprehensive and feasible research methods,etc.In order to improve the accuracy and scientificity of TCM differentiation and treatment,we consequently improved the efficiency of clinical diagnosis and treatment and promoted the objective process of TCM syndromes.This paper integrates grey model and Markov model to discuss the scientific application of Grey Markov model in TCM syndrome prediction,centering on the characteristics of syndromes of TCM,such as inclusion,dynamics,imbalance and complexity.
引文
[1] 杨爽,刘恩顺,孙增涛.中医证候研究中数理统计方法应用现状[J].长春中医药大学学报,2013,29(2):210-212.
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[3] 孙喜灵,郑秋生,林霞,等.中医证候结构表征研究及其前景展望[J].世界中医药,2015,10(2):272-275.
[4] 孙瑞华,李欢,徐凯,等.中医证候临床评价的探讨[J].世界中医药,2017,12(6):1470-1474.
[5] 孙喜灵,姜伟炜,于东林,等.提高中医证候主观辨识准确性的关键问题[J].中医杂志,2016,57(3):181-184.
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[9] 王利,庄燕鸿,何建成.多元统计方法在中医证候研究中的应用[J].中华中医药杂志,2016,31(12):4916-4918.
[10] 陈文萃.基于时间序列的建筑工程造价预测探究[J].价值工程,2013,32(10):56-57.
[11] Yang X,Chongsuvivatwong V,Mcneil E,et al.Developing a diagnostic checklist of traditional Chinese medicine symptoms and signs for psoriasis:a Delphi study[J].Chinese Medicine,2013,8(1):10.
[12] 蓝鋆,王晶,尤圣富,等.贝叶斯网络技术在中医证候研究中的运用[J].中国中医骨伤科杂志,2015,23(2):70-72.
[13] 侯玉梅,朱亚楠,尹福在.基于支持向量机和人工神经网络的2型糖尿病患病风险预测研究[J].现代预防医学,2017,44(11):1921-1924.
[14] 金汉,华伟,顾敏,等.心脏再同步化治疗后出现超反应的预测因子分析[J].中国循环杂志,2017,32(7):650-654.
[15] 赵红波,吴晓晶,杨云,等.慢性功能性便秘与中医体质类型及相关危险因素的Logistic回归分析[J].中医杂志,2017,58(16):1393-1398.
[16] Petreski M,Petreski B,Tumanoska D,et al.The size and effects of emigration and remittances in the Western Balkans.A forecasting based on a Delphi process[J].Südosteuropa,2018,65(4):679-695.
[17] Sun L,Liu GM.Apply Gray-Markov Model to predict construction disaster death toll[C]//International Conference on Artificial Intelligence,Management Science and Electronic Commerce.IEEE,2011:4969-4972.
[18] 虞峰.基于无偏灰色马尔科夫模型的传染病发病率预测[J].延安大学学报:医学科学版,2013,11(2):12-14.
[19] 干逸曼,朱家明,臧学琴,等.基于灰色马尔可夫模型对合肥房价的预测[J].通化师范学院学报,2017,38(6):31-34.
[20] Deng JL.Introduction to Grey system theory[J].Journal of Grey System,1989,1(1):1-24.
[21] 王振鑫,田融.灰色系统理论简介[J].内蒙古电力技术,1997(2):70-72.
[22] 童明余,周孝华,董辉.基于区间灰数与离散灰数双重异构序列的预测建模方法研究[J].统计与信息论坛,2014,29(10):3-8.
[23] 张珊珊,尚莉丽.基于灰色系统理论的中医医院门诊工作量分析及预测研究[J].合肥学院学报:自然科学版,2013,23(4):24-28.
[24] 周霞,邱宏,王鹏.灰色预测建模方法及在医学中的应用[J].数理医药学杂志,2007,20(1):73-75.
[25] 张宗震.马尔可夫预测法基本原理解析与应用[J].成都电子机械高等专科学校学报,2002(2):25-29.
[26] Li C,Ma Q,Xiao L,et al.Image splicing detection based on Markov features in QDCT domain[J].Neurocomputing,2017,228(ICIC2015):29-36.
[27] 郑岩岩,舒彤,陈收,等.外商直接投资趋势研究——基于灰色马尔可夫预测模型与时间序列模型的对比[J].系统工程理论与实践,2016,36(4):897-909.
[28] Osagiede AA,Ekhosuehi VU.Finding a continuous-time Markov chain via sparse stochastic matrices in manpower systems[J].Journal of the Nigerian Mathematical Society,2015,34(1):94-105.
[29] 严薇荣,冉鹏,徐勇,等.基于灰色马尔可夫模型的伤寒副伤寒发病率预测[J].数理医药学杂志,2008,21(2):137-139.
[30] 刘博,杜建强,聂斌,等.基于二阶HMM的中医诊断古文词性标注[J].计算机工程,2017,43(7):211-216.
[31] 田灵芝,符顺明,余爱,等.马尔可夫预测模型在伤寒和副伤寒预测中的应用[J].高师理科学刊,2015,35(7):30-32.
[32] 张磊,孙长青.高阶残差修正GM(1,1)区间预测模型及其应用[J].兵器装备工程学报,2017,38(2):177-181.
[33] 万臣,赵勇.基于灰色马尔可夫模型的住宅工程造价预测研究[J].科技和产业,2016,16(5):95-99.
[34] 俞树荣,韩竣羽,李淑欣.利用灰色马尔可夫模型预测腐蚀管道寿命[J].机械强度,2016,38(4):850-856.
[35] 闫伟,张军.基于时间序列分析的网络流量异常检测[J].吉林大学学报:理学版,2017,55(5):38-43.
[36] Li YZ,Luan R,Niu JC.Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain[C]//2008 3rd IEEE Conference on Industrial Electronics and Applications.IEEE,2009:1729-1733.
[37] 易静,胡代玉,杨德香,等.三种预测模型在肺结核发病预测中的应用[J].中国全科医学,2012,15(13):1495-1497.
[38] 郭蕾,张启明,王永炎,等.证候规范化研究的思路和方法探讨[J].中国中西医结合杂志,2006,26(3):258-261.
[39] 徐玮斐,刘国萍,王忆勤,等.近5年中医证候诊断客观化研究述评[J].中医杂志,2016,57(5):442-445.
[40] 陈渝,谢天添,林涛,等.两种马尔科夫链菜单预测模型建模[J].四川大学学报:自然科学版,2013,50(2):255-260.
[41] 蔡伟达.证候研究中常用数据分析方法的文献研究及基于隐类模型的抑郁症隐变量分析[D].北京:北京中医药大学,2015.
[42] 胡岸.灰色马尔可夫链组合在预测股票价格上的应用[J].科学技术与工程,2012,12(1):133-136.
[43] 刘子俊,孙健.基于遗传算法和神经网络的分时段风速预测方法[J].江苏电机工程,2015,34(1):6-8.
[2] Li DT,Wang J,Jiang HY,et al.Quantitative evaluation of the degrees of traditional Chinese medicine qualitative syndromes of osteoporosis[J].Journal of Integrative Medicine,2012,10(11):1254-1262.
[3] 孙喜灵,郑秋生,林霞,等.中医证候结构表征研究及其前景展望[J].世界中医药,2015,10(2):272-275.
[4] 孙瑞华,李欢,徐凯,等.中医证候临床评价的探讨[J].世界中医药,2017,12(6):1470-1474.
[5] 孙喜灵,姜伟炜,于东林,等.提高中医证候主观辨识准确性的关键问题[J].中医杂志,2016,57(3):181-184.
[6] 李梢.从维度与阶度探讨中医证候的特征及标准化方法[J].北京中医药大学学报,2003,26(3):1-4.
[7] 牟新,周迪夷,周旦阳,等.采用前瞻性队列研究在观察中医证候演变规律中的方法学探讨[J].中华中医药杂志,2014,29(2):514-517.
[8] Liu GP,Yan JJ,Wang YQ.Deep Learning Based Syndrome Diagnosis of Chronic Gastritis[J].Comput Math Methods Med,2014,2014(3):938350.
[9] 王利,庄燕鸿,何建成.多元统计方法在中医证候研究中的应用[J].中华中医药杂志,2016,31(12):4916-4918.
[10] 陈文萃.基于时间序列的建筑工程造价预测探究[J].价值工程,2013,32(10):56-57.
[11] Yang X,Chongsuvivatwong V,Mcneil E,et al.Developing a diagnostic checklist of traditional Chinese medicine symptoms and signs for psoriasis:a Delphi study[J].Chinese Medicine,2013,8(1):10.
[12] 蓝鋆,王晶,尤圣富,等.贝叶斯网络技术在中医证候研究中的运用[J].中国中医骨伤科杂志,2015,23(2):70-72.
[13] 侯玉梅,朱亚楠,尹福在.基于支持向量机和人工神经网络的2型糖尿病患病风险预测研究[J].现代预防医学,2017,44(11):1921-1924.
[14] 金汉,华伟,顾敏,等.心脏再同步化治疗后出现超反应的预测因子分析[J].中国循环杂志,2017,32(7):650-654.
[15] 赵红波,吴晓晶,杨云,等.慢性功能性便秘与中医体质类型及相关危险因素的Logistic回归分析[J].中医杂志,2017,58(16):1393-1398.
[16] Petreski M,Petreski B,Tumanoska D,et al.The size and effects of emigration and remittances in the Western Balkans.A forecasting based on a Delphi process[J].Südosteuropa,2018,65(4):679-695.
[17] Sun L,Liu GM.Apply Gray-Markov Model to predict construction disaster death toll[C]//International Conference on Artificial Intelligence,Management Science and Electronic Commerce.IEEE,2011:4969-4972.
[18] 虞峰.基于无偏灰色马尔科夫模型的传染病发病率预测[J].延安大学学报:医学科学版,2013,11(2):12-14.
[19] 干逸曼,朱家明,臧学琴,等.基于灰色马尔可夫模型对合肥房价的预测[J].通化师范学院学报,2017,38(6):31-34.
[20] Deng JL.Introduction to Grey system theory[J].Journal of Grey System,1989,1(1):1-24.
[21] 王振鑫,田融.灰色系统理论简介[J].内蒙古电力技术,1997(2):70-72.
[22] 童明余,周孝华,董辉.基于区间灰数与离散灰数双重异构序列的预测建模方法研究[J].统计与信息论坛,2014,29(10):3-8.
[23] 张珊珊,尚莉丽.基于灰色系统理论的中医医院门诊工作量分析及预测研究[J].合肥学院学报:自然科学版,2013,23(4):24-28.
[24] 周霞,邱宏,王鹏.灰色预测建模方法及在医学中的应用[J].数理医药学杂志,2007,20(1):73-75.
[25] 张宗震.马尔可夫预测法基本原理解析与应用[J].成都电子机械高等专科学校学报,2002(2):25-29.
[26] Li C,Ma Q,Xiao L,et al.Image splicing detection based on Markov features in QDCT domain[J].Neurocomputing,2017,228(ICIC2015):29-36.
[27] 郑岩岩,舒彤,陈收,等.外商直接投资趋势研究——基于灰色马尔可夫预测模型与时间序列模型的对比[J].系统工程理论与实践,2016,36(4):897-909.
[28] Osagiede AA,Ekhosuehi VU.Finding a continuous-time Markov chain via sparse stochastic matrices in manpower systems[J].Journal of the Nigerian Mathematical Society,2015,34(1):94-105.
[29] 严薇荣,冉鹏,徐勇,等.基于灰色马尔可夫模型的伤寒副伤寒发病率预测[J].数理医药学杂志,2008,21(2):137-139.
[30] 刘博,杜建强,聂斌,等.基于二阶HMM的中医诊断古文词性标注[J].计算机工程,2017,43(7):211-216.
[31] 田灵芝,符顺明,余爱,等.马尔可夫预测模型在伤寒和副伤寒预测中的应用[J].高师理科学刊,2015,35(7):30-32.
[32] 张磊,孙长青.高阶残差修正GM(1,1)区间预测模型及其应用[J].兵器装备工程学报,2017,38(2):177-181.
[33] 万臣,赵勇.基于灰色马尔可夫模型的住宅工程造价预测研究[J].科技和产业,2016,16(5):95-99.
[34] 俞树荣,韩竣羽,李淑欣.利用灰色马尔可夫模型预测腐蚀管道寿命[J].机械强度,2016,38(4):850-856.
[35] 闫伟,张军.基于时间序列分析的网络流量异常检测[J].吉林大学学报:理学版,2017,55(5):38-43.
[36] Li YZ,Luan R,Niu JC.Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain[C]//2008 3rd IEEE Conference on Industrial Electronics and Applications.IEEE,2009:1729-1733.
[37] 易静,胡代玉,杨德香,等.三种预测模型在肺结核发病预测中的应用[J].中国全科医学,2012,15(13):1495-1497.
[38] 郭蕾,张启明,王永炎,等.证候规范化研究的思路和方法探讨[J].中国中西医结合杂志,2006,26(3):258-261.
[39] 徐玮斐,刘国萍,王忆勤,等.近5年中医证候诊断客观化研究述评[J].中医杂志,2016,57(5):442-445.
[40] 陈渝,谢天添,林涛,等.两种马尔科夫链菜单预测模型建模[J].四川大学学报:自然科学版,2013,50(2):255-260.
[41] 蔡伟达.证候研究中常用数据分析方法的文献研究及基于隐类模型的抑郁症隐变量分析[D].北京:北京中医药大学,2015.
[42] 胡岸.灰色马尔可夫链组合在预测股票价格上的应用[J].科学技术与工程,2012,12(1):133-136.
[43] 刘子俊,孙健.基于遗传算法和神经网络的分时段风速预测方法[J].江苏电机工程,2015,34(1):6-8.