徐春华, 马家奇. 麻疹暴发突发公共卫生事件分级量化判定模型建立[J]. 疾病监测, 2010, 25(3): 231-234. DOI: 10.3784/j.issn.1003-9961.2010.03.021
引用本文: 徐春华, 马家奇. 麻疹暴发突发公共卫生事件分级量化判定模型建立[J]. 疾病监测, 2010, 25(3): 231-234. DOI: 10.3784/j.issn.1003-9961.2010.03.021
XU Chun-hua, MA Jia-qi. Establishment of stratified quantitative decision models for measles-related public health emergency events[J]. Disease Surveillance, 2010, 25(3): 231-234. DOI: 10.3784/j.issn.1003-9961.2010.03.021
Citation: XU Chun-hua, MA Jia-qi. Establishment of stratified quantitative decision models for measles-related public health emergency events[J]. Disease Surveillance, 2010, 25(3): 231-234. DOI: 10.3784/j.issn.1003-9961.2010.03.021

麻疹暴发突发公共卫生事件分级量化判定模型建立

Establishment of stratified quantitative decision models for measles-related public health emergency events

  • 摘要: 目的 以麻疹暴发突发公共卫生事件为例,探索研究突发公共卫生事件分级量化判定指标、量化判定模型及对模型应用的评价。 方法 使用SPSS Clementine 11.1.1软件,采用C5.0决策树与多分类有序反应变量的logistic回归分析建立模型。 结果 通过C5.0算法获得了决策树模型以及自变量指标的重新分组的标准,并进一步进行多分类有序反应变量的logistic回归分析,模型评价结果显示两者对于麻疹突发事件的级别判定和预测都具有很高的正确性。 结论 结合应用决策树模型和logistic回归模型可实现对麻疹突发事件自动进行级别判定的目的。

     

    Abstract: Objective To establish the indicators for stratifying public health emergency events relating to measles outbreaks, to develop quantitative decision models based on them, and to evaluate the model application. Methods Two models were established based on the C5.0 decision tree algorithm and the polytomous Logistic regression for ordinal response of SPSS Clementine 11.1.1. Results The decision tree model and the criteria for independent variable re-categorization were obtained by the C5.0 algorithm, and the polytomous Logistic regression model for ordinal response was also performed. The evaluation results showed that both models had high accuracy in determining the level of measles-related public health emergency events and predicting outbreaks. Conclusion With the combined application of the decision tree model and the Logistic regression model, determination of measles-related public emergency levels can be automatically achieved. This research was funded by Development and Application of Feasible Information Technology for Disease Prevention and Control (No.2008BAI56B06)

     

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