Medical coding is a complex task, requiring assignment of a subset of over
72,000 ICD codes to a patient's notes. Modern natural language processing
approaches to these tasks have been challenged by the length of the input and
size of the output space. We limit our model inputs to a small window around
medical entities found in our documents. From those local contexts, we build
contextualized representations of both ICD codes and entities, and aggregate
over these representations to form document-level predictions. In contrast to
existing methods which use a representation fixed either in size or by codes
seen in training, we represent ICD codes by encoding the code description with
local context. We discuss metrics appropriate to deploying coding systems in
practice. We show that our approach is superior to existing methods in both
standard and deployable measures, including performance on rare and unseen
codes.

本文介绍了一种将自然语言处理应用于医学编码的方法，该方法限制了模型输入的范围并构建基于本地上下文的 ICD 代码和实体的上下文表示，并在此基础上进行文献级预测，这种方法在处理所有类型的 ICD 代码方面表现出优异的效果，包括在处理罕见和未见过的代码时也表现出色。