基于结构方程模型的疾病危险因素研究
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摘要
【目的】利用结构方程模型分析客观指标数据,对与疾病相关的危险因素进行挖掘。【方法】利用文献研究、线性相关分析方法提取疾病危险因素,使用结构方程模型对这些危险因素进行分析;使用分类回归树(Classification And Regression Tree, CART)算法构建疾病诊断模型,利用诊断模型对危险因素进行定性、定量评价及对比分析。【结果】挖掘出9个与疾病相关的危险因素,经定量评价后,基于结构方程模型的疾病危险因素诊断模型各项指标均处于较高水平,且整体性能更好。【局限】实验数据量有限。【结论】基于结构方程模型的疾病危险因素能够提高疾病的早期诊断率,可以辅助临床决策。
[Objective] This paper aims to use the structural equation model to analyze the objective index data and explore the risk factors related to the disease. [Methods] Based on literature research and linear correlation analysis,this paper extracts disease risk factors. Structural Equation modeling was used to analyze these risk factors. The disease diagnosis model was constructed using the classification regression tree (CART) algorithm, and risk factors were qualitatively and quantitatively evaluated and compared using diagnostic models. [Results] Nine risk factors related to disease were discovered. After quantitative evaluation, the indicators of disease risk factors diagnosis model based on Structural Equation Modeling were at a high level, and the overall performance was better. [Limitations] The amount of experimental data is limited, and the amount of data can be expanded to conduct experiments in the future.[Conclusions] Disease risk factors based on structural equation model can improve the early diagnosis rate of disease and can assist clinical decision-making.
[Objective] This paper aims to use the structural equation model to analyze the objective index data and explore the risk factors related to the disease. [Methods] Based on literature research and linear correlation analysis,this paper extracts disease risk factors. Structural Equation modeling was used to analyze these risk factors. The disease diagnosis model was constructed using the classification regression tree (CART) algorithm, and risk factors were qualitatively and quantitatively evaluated and compared using diagnostic models. [Results] Nine risk factors related to disease were discovered. After quantitative evaluation, the indicators of disease risk factors diagnosis model based on Structural Equation Modeling were at a high level, and the overall performance was better. [Limitations] The amount of experimental data is limited, and the amount of data can be expanded to conduct experiments in the future.[Conclusions] Disease risk factors based on structural equation model can improve the early diagnosis rate of disease and can assist clinical decision-making.
引文
[1]罗旭,刘友江.医疗大数据研究现状及其临床应用[J].医学信息学杂志,2015,36(5):10-14.(Luo Xu,Liu Youjiang.Medical Big Data Research Status and Its Clinical Application[J].Journal of Medical Informatics,2015,36(5):10-14.)
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[4]马费成.推进大数据、人工智能等信息技术与人文社会科学研究深度融合[N].光明日报,2018-07-29(6).(Ma Feicheng.Promoting the Deep Integration of Information Technology and Humanities and Social Science Research,such as Big Data and Artificial Intelligence[N].Guangming Daily,2018-07-29(6).)
[5]麦忠海.结构方程模型之应用问题研究--基于2014年广东省国民幸福感影响因素数据[D].广州:广东财经大学,2015.(Mai Zhonghai.Study on Application Problems of Structural Equation Model--Based on the Data of the National Happiness Influencing Factors of Guangdong Province in 2014[D].Guangzhou:Guangdong University of Finance&Economics,2015.)
[6]辛士波,陈妍,张宸.结构方程模型理论的应用研究成果综述[J].工业技术经济,2014(5):61-71.(Xin Shibo,Chen Yan,Zhang Chen.Review on Research and Application of Structural Equation Model[J].Journal of Industrial Technological Economics,2014(5):61-71.)
[7]陈星光.基于结构方程模型的软实力测度与评估[J].统计与决策,2014(6):19-21.(Chen Xingguang.Soft Power Measurement and Evaluation Based on Structural Equation Modeling[J].Statistics and Decision,2014(6):19-21.)
[8]蔡瑞初,陈薇,张坤,等.基于非时序观察数据的因果关系发现综述[J].计算机学报,2017,40(6):1470-1490.(Cai Ruichu,Chen Wei,Zhang Kun,et al.A Survey on Non-Temporal Series Observational Data Based Causal Discovery[J].Journal of Computers,2017,40(6):1470-1490.)
[9]金洲.基于约束学习的观测数据因果关系发现研究[D].合肥:中国科学技术大学,2014.(Jin Zhou.Study of Causal Relationship Discovery Using Constrain-based Method from Observational Data[D].Hefei:University of Science and Technology of China,2014.)
[10]Cai R,Zhang Z,Hao Z.Causal Gene Identification Using Combinatorial V-Structure Search[J].Neural Networks,2013,43:63-71.
[11]张润梅.基于贝叶斯网络的复杂系统因果关系研究[D].合肥:合肥工业大学,2015.(Zhang Runmei.Research on Causality in Complex System Based on Bayesian Network[D].Hefei:Hefei University of Technology,2015.)
[12]Chen W,Hao Z,Cai R,et al.Multiple-Cause Discovery Combined with Structure Learning for High-Dimensional Discrete Data and Application to Stock Prediction[J].Soft Computing,2016,20(11):4575-4588.
[13]郝志峰,谢蔚涛,蔡瑞初,等.基于因果强度的时序因果关系发现算法[J].计算机工程与设计,2017,38(1):132-137.(Hao Zhifeng,Xie Weitao,Cai Ruichu,et al.Casual Inference on Time Series Using Causal Strength[J].Computer Engineering and Design,2017,38(1):132-137.)
[14]袁畅.基于时序的社交网络因果关系发现[D].广州:广东工业大学,2016.(Yuan Chang.A Minimal Description Length Approach for Social Causal Discovery[D].Guangzhou:Guangdong University of Technology,2016.)
[15]Detilleux J,Theron L,Beduin J M,et al.A Structural Equation Model to Evaluate Direct and Indirect Factors Associated with a Latent Measure of Mastitis in Belgian Dairy Herds[J].Preventive Veterinary Medicine,2012,107(3-4):170-179.
[16]Emmann C H,Arens L,Theuvsen L.Individual Acceptance of the Biogas Innovation:A Structural Equation Model[J].Energy Policy,2013,62:372-378.
[17]Kim S H,Cha J M,Singh A J,et al.A Longitudinal Investigation to Test the Validity of the American Customer Satisfaction Model in the U.S.Hotel Industry[J].International Journal of Hospitality Management,2013,35:193-202.
[18]钟茂华,田向亮,刘畅,等.基于结构方程模型的地铁乘客安全行为影响因素分析[J].中国安全生产科学技术,2018,14(1):5-11.(Zhong Maohua,Tian Xiangliang,Liu Chang,et al.Analysis on Factors of Safety Behavior for Metro Passengers Based on Structural Equation Model[J].Journal of Safety Science and Technology,2018,14(1):5-11.)
[19]潘丹,罗帆.基于结构方程模型的建筑施工项目安全绩效评价[J].安全与环境学报,2018,18(2):602-609.(Pan Dan,Luo Fan.Safety Performance Evaluation of the Construction Projects Based on the Structural Equation Model[J].Journal of Safety and Environment,2018,18(2):602-609.)
[20]吴永定,廖剑锋,黄美娟,等.应用结构方程模型探讨社区卫生服务满意度的影响因素[J].中国卫生统计,2018,35(2):219-221.(Wu Yongding,Liao Jianfeng,Huang Meijuan,et al.Applying Structural Equation Modeling to Explore the Influencing Factors of Community Health Service Satisfaction[J].Chinese Journal of Health Statistics,2018,35(2):219-221.)
[21]李柏桐,郭汉丁,伍红民.基于PLS-SEM模型的我国节能服务产业竞争力形成机理研究[J].科技管理研究,2018(14):105-110.(Li Botong,Guo Handing,Wu Hongmin.Research on Competitiveness Formation Mechanism of China’s Energy-saving Service Industry Based on PLS-SEMModel[J].Science and Technology Management Research,2018(14):105-110.)
[22]赵书亮.2型糖尿病合并抑郁的影响因素研究[D].北京:北京师范大学,2012.(Zhao Shuliang.The Study on the Related Factors of Coexisting Depression in Patients with Diabetes Mellitus[D].Beijing:Beijing Normal University,2012.)
[23]李欣欣,董丽敏,刘晓英,等.基于结构方程模型探讨哮喘患者治疗依从性影响因素[J].中华疾病控制杂志,2017,21(2):187-191.(Li Xinxin,Dong Limin,Liu Xiaoying,et al.To Explore the Influencing Factors of Treatment Compliance of Asthma Patients Based on Structural Equation Modeling[J].Chinese Journal of Disease Control&Prevention,2017,21(2):187-191.)
[24]江海冰,李金梅,胡真真,等.基于PLS-SEM模型的老年人慢性病影响因素分析[J].实用预防医学,2018(2):132-136.(Jiang Haibing,Li Jinmei,Hu Zhenzhen,et al.Factors Influencing Chronic Diseases of the Elderly Based on PLS-SEM Model[J].Practical Preventive Medicine,2018(2):132-136.)
[25]中国医学科学院.国家人口与健康科学数据共享服务平台[EB/OL].[2018-06-01]http://www.ncmi.cn/.(Chinese Academy of Medical Sciences.National Population and Health Science Data Sharing Service Platform[EB/OL].[2018-06-01]http://www.ncmi.cn/.)
[26]Tenenhaus M,Amato S,Esposito Vinzi V.A Global Goodness-of-Fit Index for PLS Structural Equation Modelling[C]//Proceedings of the 42nd SIS Scientific Meeting.2004:739-742.
[27]张爱华,赵国龙.线下熟悉度在社交网络信任中的调节作用研究[J].北京邮电大学学报:社会科学版,2015,17(1):18-24.(Zhang Aihua,Zhao Guolong.Adjusting Role of Offline Familiarity in Social Network Trust[J].Journal of Beijing University of Posts and Telecommunications:Social Science Edition,2015,17(1):18-24.)
[2]马锡坤,杨国斌,于京杰.国内电子病历发展与应用现状分析[J].计算机应用与软件,2015,32(1):10-12.(Ma Xikun,Yang Guobin,Yu Jingjie.Analysing the Development and Application Status of Electronic Medical Records in China[J].Computer Applications&Software,2015,32(1):10-12.)
[3]张振,周毅,杜守洪,等.医疗大数据及其面临的机遇与挑战[J].医学信息学杂志,2014,35(6):2-8.(Zhang Zhen,Zhou Yi,Du Shouhong,et al.Medical Big Data and the Facing Opportunities and Challenges[J].Journal of Medical Informatics,2014,35(6):2-8.)
[4]马费成.推进大数据、人工智能等信息技术与人文社会科学研究深度融合[N].光明日报,2018-07-29(6).(Ma Feicheng.Promoting the Deep Integration of Information Technology and Humanities and Social Science Research,such as Big Data and Artificial Intelligence[N].Guangming Daily,2018-07-29(6).)
[5]麦忠海.结构方程模型之应用问题研究--基于2014年广东省国民幸福感影响因素数据[D].广州:广东财经大学,2015.(Mai Zhonghai.Study on Application Problems of Structural Equation Model--Based on the Data of the National Happiness Influencing Factors of Guangdong Province in 2014[D].Guangzhou:Guangdong University of Finance&Economics,2015.)
[6]辛士波,陈妍,张宸.结构方程模型理论的应用研究成果综述[J].工业技术经济,2014(5):61-71.(Xin Shibo,Chen Yan,Zhang Chen.Review on Research and Application of Structural Equation Model[J].Journal of Industrial Technological Economics,2014(5):61-71.)
[7]陈星光.基于结构方程模型的软实力测度与评估[J].统计与决策,2014(6):19-21.(Chen Xingguang.Soft Power Measurement and Evaluation Based on Structural Equation Modeling[J].Statistics and Decision,2014(6):19-21.)
[8]蔡瑞初,陈薇,张坤,等.基于非时序观察数据的因果关系发现综述[J].计算机学报,2017,40(6):1470-1490.(Cai Ruichu,Chen Wei,Zhang Kun,et al.A Survey on Non-Temporal Series Observational Data Based Causal Discovery[J].Journal of Computers,2017,40(6):1470-1490.)
[9]金洲.基于约束学习的观测数据因果关系发现研究[D].合肥:中国科学技术大学,2014.(Jin Zhou.Study of Causal Relationship Discovery Using Constrain-based Method from Observational Data[D].Hefei:University of Science and Technology of China,2014.)
[10]Cai R,Zhang Z,Hao Z.Causal Gene Identification Using Combinatorial V-Structure Search[J].Neural Networks,2013,43:63-71.
[11]张润梅.基于贝叶斯网络的复杂系统因果关系研究[D].合肥:合肥工业大学,2015.(Zhang Runmei.Research on Causality in Complex System Based on Bayesian Network[D].Hefei:Hefei University of Technology,2015.)
[12]Chen W,Hao Z,Cai R,et al.Multiple-Cause Discovery Combined with Structure Learning for High-Dimensional Discrete Data and Application to Stock Prediction[J].Soft Computing,2016,20(11):4575-4588.
[13]郝志峰,谢蔚涛,蔡瑞初,等.基于因果强度的时序因果关系发现算法[J].计算机工程与设计,2017,38(1):132-137.(Hao Zhifeng,Xie Weitao,Cai Ruichu,et al.Casual Inference on Time Series Using Causal Strength[J].Computer Engineering and Design,2017,38(1):132-137.)
[14]袁畅.基于时序的社交网络因果关系发现[D].广州:广东工业大学,2016.(Yuan Chang.A Minimal Description Length Approach for Social Causal Discovery[D].Guangzhou:Guangdong University of Technology,2016.)
[15]Detilleux J,Theron L,Beduin J M,et al.A Structural Equation Model to Evaluate Direct and Indirect Factors Associated with a Latent Measure of Mastitis in Belgian Dairy Herds[J].Preventive Veterinary Medicine,2012,107(3-4):170-179.
[16]Emmann C H,Arens L,Theuvsen L.Individual Acceptance of the Biogas Innovation:A Structural Equation Model[J].Energy Policy,2013,62:372-378.
[17]Kim S H,Cha J M,Singh A J,et al.A Longitudinal Investigation to Test the Validity of the American Customer Satisfaction Model in the U.S.Hotel Industry[J].International Journal of Hospitality Management,2013,35:193-202.
[18]钟茂华,田向亮,刘畅,等.基于结构方程模型的地铁乘客安全行为影响因素分析[J].中国安全生产科学技术,2018,14(1):5-11.(Zhong Maohua,Tian Xiangliang,Liu Chang,et al.Analysis on Factors of Safety Behavior for Metro Passengers Based on Structural Equation Model[J].Journal of Safety Science and Technology,2018,14(1):5-11.)
[19]潘丹,罗帆.基于结构方程模型的建筑施工项目安全绩效评价[J].安全与环境学报,2018,18(2):602-609.(Pan Dan,Luo Fan.Safety Performance Evaluation of the Construction Projects Based on the Structural Equation Model[J].Journal of Safety and Environment,2018,18(2):602-609.)
[20]吴永定,廖剑锋,黄美娟,等.应用结构方程模型探讨社区卫生服务满意度的影响因素[J].中国卫生统计,2018,35(2):219-221.(Wu Yongding,Liao Jianfeng,Huang Meijuan,et al.Applying Structural Equation Modeling to Explore the Influencing Factors of Community Health Service Satisfaction[J].Chinese Journal of Health Statistics,2018,35(2):219-221.)
[21]李柏桐,郭汉丁,伍红民.基于PLS-SEM模型的我国节能服务产业竞争力形成机理研究[J].科技管理研究,2018(14):105-110.(Li Botong,Guo Handing,Wu Hongmin.Research on Competitiveness Formation Mechanism of China’s Energy-saving Service Industry Based on PLS-SEMModel[J].Science and Technology Management Research,2018(14):105-110.)
[22]赵书亮.2型糖尿病合并抑郁的影响因素研究[D].北京:北京师范大学,2012.(Zhao Shuliang.The Study on the Related Factors of Coexisting Depression in Patients with Diabetes Mellitus[D].Beijing:Beijing Normal University,2012.)
[23]李欣欣,董丽敏,刘晓英,等.基于结构方程模型探讨哮喘患者治疗依从性影响因素[J].中华疾病控制杂志,2017,21(2):187-191.(Li Xinxin,Dong Limin,Liu Xiaoying,et al.To Explore the Influencing Factors of Treatment Compliance of Asthma Patients Based on Structural Equation Modeling[J].Chinese Journal of Disease Control&Prevention,2017,21(2):187-191.)
[24]江海冰,李金梅,胡真真,等.基于PLS-SEM模型的老年人慢性病影响因素分析[J].实用预防医学,2018(2):132-136.(Jiang Haibing,Li Jinmei,Hu Zhenzhen,et al.Factors Influencing Chronic Diseases of the Elderly Based on PLS-SEM Model[J].Practical Preventive Medicine,2018(2):132-136.)
[25]中国医学科学院.国家人口与健康科学数据共享服务平台[EB/OL].[2018-06-01]http://www.ncmi.cn/.(Chinese Academy of Medical Sciences.National Population and Health Science Data Sharing Service Platform[EB/OL].[2018-06-01]http://www.ncmi.cn/.)
[26]Tenenhaus M,Amato S,Esposito Vinzi V.A Global Goodness-of-Fit Index for PLS Structural Equation Modelling[C]//Proceedings of the 42nd SIS Scientific Meeting.2004:739-742.
[27]张爱华,赵国龙.线下熟悉度在社交网络信任中的调节作用研究[J].北京邮电大学学报:社会科学版,2015,17(1):18-24.(Zhang Aihua,Zhao Guolong.Adjusting Role of Offline Familiarity in Social Network Trust[J].Journal of Beijing University of Posts and Telecommunications:Social Science Edition,2015,17(1):18-24.)
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