Sanz José, Paternain Daniel, Galar Mikel, Fernandez Javier, Reyero Diego, Belzunegui Tomás
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
Comput Methods Programs Biomed. 2017 Apr;142:1-8. doi: 10.1016/j.cmpb.2017.02.011. Epub 2017 Feb 10.
Severe trauma patients are those who have several injuries implying a death risk. Prediction systems consider the severity of these injuries to predict whether the patients are likely to survive or not. These systems allow one to objectively compare the quality of the emergency services of trauma centres across different hospitals. However, even the most accurate existing prediction systems are based on the usage of a single model. The aim of this paper is to combine several models to make the prediction, since this methodology usually improves the performance of single models.
The two currently used prediction systems by the Hospital of Navarre, which are based on logistic regression models, besides the C4.5 decision tree are combined to conform our proposed multiple classifier system. The quality of the method is tested using the major trauma registry of Navarre, which stores information of 462 trauma patients. A 10x10-fold cross-validation model is applied using as performance measures the specificity, sensitivity and the geometric mean between the two former ones. The results are supported by the usage of the Mann-Whitney's U statistical test.
The proposed method provides 0.8908, 0.6703 and 0.7661 for sensitivity, specificity and geometric mean, respectively. It slightly decreases the sensitivity of the currently used systems but it notably increases the specificity, which implies a large enhancement on the geometric mean. The same behaviour is found when it is compared versus four classical ensemble approaches and the random forest. The statistical analysis supports the quality of our proposal, since the obtained p-values are less than 0.01 in all the cases.
The obtained results show that the multiple classifier systems is the best choice among the considered methods to obtain a trade-off between sensitivity and specificity.
严重创伤患者是指有多处损伤且存在死亡风险的患者。预测系统通过考量这些损伤的严重程度来预测患者是否可能存活。这些系统能让人们客观地比较不同医院创伤中心的急诊服务质量。然而，即便现存最精确的预测系统也基于单一模型的使用。本文旨在组合多个模型进行预测，因为这种方法通常能提升单一模型的性能。
纳瓦拉医院目前使用的两种基于逻辑回归模型的预测系统，再加上C4.5决策树，被组合起来构成我们提出的多分类器系统。使用纳瓦拉的主要创伤登记处来测试该方法的质量，该登记处存储了462名创伤患者的信息。应用10×10倍交叉验证模型，将特异性、敏感性以及前两者之间的几何平均数用作性能指标。通过使用曼 - 惠特尼U统计检验来支持结果。
所提出的方法的敏感性、特异性和几何平均数分别为0.8908、0.6703和0.7661。它略微降低了当前使用系统的敏感性，但显著提高了特异性，这意味着几何平均数有大幅提升。与四种经典集成方法和随机森林相比时也发现了同样的情况。统计分析支持了我们提议的质量，因为在所有情况下获得的p值均小于0.01。
所得结果表明，在考虑的方法中，多分类器系统是在敏感性和特异性之间取得平衡的最佳选择。
