Generalized Linear Models, Generalized Additive Models and Neural Networks: Comparative Study in Medical Applications
Generalized Linear Models, Generalized Additive Models and Neural Networks: Comparative Study in Medical Applications
复制标题
广义线性模型、广义加性模型和神经网络：医学应用中的比较研究
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Patrícia Xufre
中科院分区：
文献类型：
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作者：
A. Papoila;C. Rocha;C. Geraldes;Patrícia Xufre
在过去的二十年里，评估疾病的严重性和预测危重患者的死亡率成为世界各地在重症监护病房工作的所有专业人员的主要关切。由于响应变量的二元性，Logistic回归模型是对这类数据进行建模的自然选择。本研究的目的是比较广义线性模型(GLM)和二元反应(McCullagh和Neld，广义线性模型)的性能。Chapman和Hall，伦敦，1989)，具有二元响应的广义加性模型(GAM)的性能(Hastie和Tibshiani，广义加性模型)。Chapman和Hall，纽约，1990)，以及人工神经网络(ANN)的性能(Bishop，用于模式识别的神经网络)。克拉伦登出版社，牛津，1995)，关于他们的预测力和鉴别力。收集了996名患者的数据集，整个样本用于模型的开发和验证过程，因为不存在外部的、独立的数据集。对所提出的方法的性能进行了评估，不仅通过使用校准图评估观察死亡率和预测死亡概率之间的一致性，而且还通过接收者工作特征(ROC)曲线下的面积来衡量它们的区分能力。
During the last two decades, evaluating severity of illness and predicting mortality of critical patients became a major concern of all professionals that work in intensive care units all over the world. Due to the binary nature of the response variable, logistic regression models were a natural choice for modelling this kind of data. The objective of this study is to compare the performance of generalized linear models (GLMs) with binary response (McCullagh and Nelder, Generalized Linear Models. Chapman and Hall, London, 1989), with the performance of generalized additive models (GAMs) with binary response (Hastie and Tibshirani, Generalized Additive Models. Chapman and Hall, New York, 1990) and also with the performance of artificial neural networks (ANNs) (Bishop, Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995), in what concerns their predictive and discriminative power. A dataset of 996 patients was collected and the entire sample was used for the development of the models and also for the validation process, due to the nonexistence of an external, independent dataset. The performance of the proposed methodologies was assessed, not only by the evaluation of the agreement between observed mortality and predicted probabilities of death through the use of calibration plots, but also by their discriminating ability, measured by the area under the receiver operating characteristic (ROC) curve.
影响因子：
9.6
作者：
KNAUS, WA;WAGNER, DP;HARRELL, FE
通讯作者：
HARRELL, FE