[1]隋美丽),申远方),黄学勇),等.分类树模型在重症手足口病风险预测中的应用*[J].郑州大学学报(医学版),2015,(01):20.
 SUI Meili),SHEN Yuanfang),HUANG Xueyong),et al.Application of risk prediction model for severe handfootmouth disease by classification tree[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2015,(01):20.
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分类树模型在重症手足口病风险预测中的应用*
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2015年01期
页码:
20
栏目:
论著
出版日期:
2015-01-20

文章信息/Info

Title:
Application of risk prediction model for severe handfootmouth disease by classification tree
作者:
隋美丽1)申远方2)黄学勇3)杨海燕1)马晓梅1)李懿3)冯慧芬4)段广才1)#
1)郑州大学公共卫生学院流行病学教研室 郑州 4500012)郑州市儿童医院感染科 郑州 4500533)河南省疾病预防控制中心传染病所 郑州 4500164)郑州大学第五附属医院感染科 郑州 450052
Author(s):
SUI Meili1)SHEN Yuanfang2)HUANG Xueyong3)YANG Haiyan1)MA Xiaomei1)LI Yi3)FENG Huifen4)DUAN Guangcai1)
1)Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 4500012)Department of Infectious Diseases, Zhengzhou Childrens Hospital, Zhengzhou 4500533)Institute for Communicable Disease Control and Prevention, Henan Center for Disease Control and Prevention, Zhengzhou 4500164)Department of Infectious Diseases, the Fifth Affiliated Hospital, Zhengzhou University, Zhengzhou 450052
关键词:
重症手足口病分类树危险因素预测模型
Keywords:
severe handfootmouth disease classification tree risk factor prediction model
分类号:
R181.3
摘要:
摘要目的:应用分类树模型构建重症手足口病的预测模型,并评价其应用价值。方法:整群抽取河南省郑州市某医院2013年4月至6月住院治疗的221例发病时间≤72 h的手足口病患儿为研究对象,采用CHAID分类树算法建立重症手足口病的预测模型,采用错分概率Risk值、索引图及受试者工作特征曲线评价模型的应用价值。结果:所建立的分类树模型包括3层,共9个结点,共筛选出4个解释变量:精神差、易惊、热峰≥39 ℃、手足抖动;其中最为重要的预测因素为精神差和易惊。模型错分概率Risk值为0.045,模型拟合的效果较好。结论:分类树模型不仅能有效地拟合重症手足口病的风险预测,还可以对变量间的交互作用进行有效的筛选。
Abstract:
AbstractAim: To establish a risk prediction model for severe handfootmouth disease(HFMD), and to evaluate its application value for severe HFMD patients. Methods: A total of 221 cases of HFMD within 72 hours who admitted to a hospital in Zhengzhou from April to June of 2013 were cluster selected for questionnaire investigation. The clinical data and laboratory parameters of the patients were collected to analyze the main factors for severe HFMD by making the use of the CHAID classification tree algorithm. The value of the established model was evaluated by the Risk statistics, index map and ROC curve. Results: The model had 3 stratums and 9 nodes. There were 4 explanatory variables screened out in the model, including poor spirit, easy to panic, top temperature above 39 ℃ and shake of hands and feet. Poor spirit and easy to panic were the most important risk factors. The risk value of misclassification probability of the model was 0.045, and the classification tree model fitted the actuality very well. Conclusion: Classification tree model can not only properly predict severe HFMD in children, but also reveal the complex interaction effects among the factors.

参考文献/References:

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备注/Memo

备注/Memo:
*国家自然科学基金资助项目81172740 #通信作者,男,1958年8月生,博士,教授,研究方向:分子流行病学和传染病流行病学,E-mail:gcduan@zzu.edu.cn
更新日期/Last Update: 1900-01-01