  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.
