Odds ratio function estimation using a generalized additive neural network
Odds ratio function estimation using a generalized additive neural network
复制标题
使用广义加性神经网络进行优势比函数估计
DOI：
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发表时间：
2019
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：
Carlos Brás;A. Papoila;Patrícia Xufre
在生物医学研究中，广义人工神经网络（GANNs）已被提出作为多层感知器的替代方案，因为它们具有更强的生成更可解释结果的能力。GANNs的灵感来自统计广义加性模型（GAM），由于ANN和GAM之间可以建立并行性，GAM的进步很自然地被纳入神经网络领域。最近提出了一种具有灵活链接函数的GANN，其结果类似于具有相同类型链接函数的GAM。然而，在医学领域，必须引入更多的改进，以获得更可解释的，因此更有用的人工神经网络。在这项研究中，提出了一种估计连续协变量的比值比函数的算法，这增加了GANN的可解释性。
In biomedical research, generalized artificial neural networks (GANNs) have been proposed as an alternative to a multi-layer perceptron owing to their greater ability to generate more interpretable results. GANNs were inspired by statistical generalized additive models (GAMs), and because of the parallelism that can be established between ANNs and GAMs, it is natural for advances in GAMs to be incorporated into the field of neural networks. A GANN with a flexible link function was recently proposed, with results similar to those of a GAM with the same type of link function. However, in the medical field, more improvements must be introduced to obtain even more interpretable, and consequently more useful, ANNs. In this study, an algorithm for estimating the odds ratio function for continuous covariates is proposed, which increases the interpretability of a GANN.