论文标题
流行病学家的深度学习：神经网络入门
Deep Learning for Epidemiologists: An Introduction to Neural Networks
论文作者
论文摘要
深度学习方法越来越多地应用于医学和医疗保健中的问题。但是，很少有流行病学家接受这些方法的正式培训。为了弥合这一差距，本文从流行病学的角度介绍了深度学习的基础。具体而言，本文回顾了机器学习中的核心概念（过度拟合，正则化，超参数），解释了几种基本的深度学习体系结构（卷积神经网络，经常性神经网络），并总结了模型的培训，评估和部署。我们的目的是使读者能够与深度学习的医学应用互动并进行批判性评估，从而促进计算机科学家与流行病学家之间的对话，以提高该技术应用的安全性和功效。
Deep learning methods are increasingly being applied to problems in medicine and healthcare. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces to the fundamentals of deep learning from an epidemiological perspective. Specifically, this article reviews core concepts in machine learning (overfitting, regularization, hyperparameters), explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks), and summarizes training, evaluation, and deployment of models. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning, facilitating a dialogue between computer scientists and epidemiologists that will improve the safety and efficacy of applications of this technology.
