  Clinical notes in healthcare facilities are tagged with the International
Classification of Diseases (ICD) code; a list of classification codes for
medical diagnoses and procedures. ICD coding is a challenging multilabel text
classification problem due to noisy clinical document inputs and long-tailed
label distribution. Recent automated ICD coding efforts improve performance by
encoding medical notes and codes with additional data and knowledge bases.
However, most of them do not reflect how human coders generate the code: first,
the coders select general code categories and then look for specific
subcategories that are relevant to a patient's condition. Inspired by this, we
propose a two-stage decoding mechanism to predict ICD codes. Our model uses the
hierarchical properties of the codes to split the prediction into two steps: At
first, we predict the parent code and then predict the child code based on the
previous prediction. Experiments on the public MIMIC-III data set show that our
model performs well in single-model settings without external data or
knowledge.
