Computational medicine, public health, and biotechnology : building a man in the machine最新文献

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CLINICAL/MEDICAL OUTCOME PREDICTION BY NEURAL NETWORKS WITH STATISTICAL ENHANCEMENT. 用神经网络预测临床/医疗结果，并进行统计增强。

Computational medicine, public health, and biotechnology : building a man in the machine

Pub Date : 1995-01-01

Toyoko S Yamashita, Isaac F Nuamah, Philip A Dorsey, Seyed M Hosseini-Nezhad, Roger A Bielefeld, Edward F Kerekes, Lynn T Singer

Neural networks offer a powerful new approach to information processing through their ability to generalize from a specific training data set. The success of this approach has raised interesting new possibilities of incorporating statistical methodology in order to enhance their predictive ability. This paper reports on two complementary methods of prediction. one using neural networks and the other using traditional statistical methods. The two methods are compared on the basis of their prediction applied to standardized developmental infant outcome measures using preselected infant and maternal variables measured at birth. Three neural network algorithms were employed. In our study, no one network outperformed the other two consistently. The neural networks provided significantly better results than the regression model in terms of variation and prediction of extreme outcomes. Finally we demonstrated that selection of relevant input variables through statistical means can produce a reduced network structure with no loss in predictive ability.

神经网络能够从特定的训练数据集中进行归纳，为信息处理提供了一种强大的新方法。这种方法的成功为结合统计方法以提高预测能力提供了有趣的新可能性。本文报告了两种互补的预测方法，一种使用神经网络，另一种使用传统统计方法。根据这两种方法对标准化婴儿发育结果测量的预测，并使用出生时测量的预选婴儿和母亲变量，对这两种方法进行了比较。我们采用了三种神经网络算法。在我们的研究中，没有一个网络的性能持续优于其他两个网络。在极端结果的变化和预测方面，神经网络的结果明显优于回归模型。最后，我们证明了通过统计手段选择相关输入变量可以在不损失预测能力的情况下减少网络结构。

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