Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast paper-to-paper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the field's ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the world's largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such data-hungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the "goodness" of deep-learning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.

中文翻译：

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电子健康记录的深度学习：多个深度神经架构的比较回顾。

尽管最近在深度学习模型方面取得了进展，但它们在临床决策支持系统中的应用非常有限。然而，最近的健康记录数字化为评估此类技术在医疗保健中的可用性提供了一个很好的平台。因此，该领域开始看到越来越多的研究论文采用电子健康记录 (EHR) 的深度学习来对风险和健康轨迹进行个性化预测。虽然这可能是一个有希望的趋势，但纸对纸的巨大差异（从他们使用的数据源和模型到他们试图回答的临床问题）阻碍了该领域针对给定的感兴趣的应用简单地比较和对比这些模型的能力. 因此，在本文中，我们的目标是对应用于 EHR 数据的关键深度学习架构进行比较回顾。此外，我们还旨在：(1) 引入和使用世界上最大和最复杂的链接初级保健 EHR 数据集之一（即临床实践研究数据链，或 CPRD）作为训练此类数据饥渴模型的新资产；(2) 提供使用 EHR 数据进行深度学习的指南；(3) 分享一些评估深度学习模型在临床风险预测中的“优劣”的最佳实践；(4) 并提出未来的研究思路，使深度学习模型更适合 EHR 数据。我们的结果突出了处理高度不平衡数据集的困难，