Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input features on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous features that were randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR features only; EMR and extraneous features; extraneous features only) were trained to predict three clinical outcomes: in-ICU mortality, 72-h ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the inclusion of extraneous features to EMR variables were negligible.

中文翻译：

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外部功能对临床任务中递归神经网络模型的性能的影响。

电子病历（EMR）是患者信息的丰富来源，包括反映生理征象和所用疗法的测量值。确定哪些变量或特征可用于预测临床结果可能具有挑战性。诸如深度神经网络之类的高级算法被设计为处理包含其测量形式的变量的高维输入，从而绕过单独的特征选择或工程步骤。我们通过在输入向量中包括从理论和经验分布中随机抽取的外部特征，来研究外部输入特征对循环神经网络（RNN）预测性能的影响。使用不同输入向量的RNN模型（仅限EMR功能； EMR和无关功能；仅外部功能）经过培训以预测三种临床结果：ICU内死亡率，72小时ICU再次入院和30天无ICU的天数。在EMR变量中包含无关功能的情况下，RNN的预测性能的降低的测量值可以忽略不计。