Electronic Health Records have become popular sources of data for secondary
research, but their use is hampered by the amount of effort it takes to
overcome the sparsity, irregularity, and noise that they contain. Modern
learning architectures can remove the need for expert-driven feature
engineering, but not the need for expert-driven preprocessing to abstract away
the inherent messiness of clinical data. This preprocessing effort is often the
dominant component of a typical clinical prediction project. In this work we
propose using semantic embedding methods to directly couple the raw, messy
clinical data to downstream learning architectures with truly minimal
preprocessing. We examine this step from the perspective of capturing and
encoding complex data dependencies in the data representation instead of in the
model, which has the nice benefit of allowing downstream processing to be done
with fast, lightweight, and simple models accessible to researchers without
machine learning expertise. We demonstrate with three typical clinical
prediction tasks that the highly compressed, embedded data representations
capture a large amount of useful complexity, although in some cases the
compression is not completely lossless.

本文提出使用语义嵌入方法，将原始、杂乱的临床数据直接与下游学习架构耦合，以实现最小化预处理。我们从捕捉和编码数据表示中的复杂数据依赖性的角度考虑这一步骤，而不是在模型中实现，该方法允许使用快速，轻量级和简单的模型进行下游处理，对于没有机器学习专业知识的研究人员非常有用。我们通过三个典型的临床预测任务证明了高度压缩的嵌入数据表示捕获了大量有用的复杂性，尽管在某些情况下，压缩并非完全无损。