  International Classification of Diseases(ICD) is an authoritative health care
classification system of different diseases and conditions for clinical and
management purposes. Considering the complicated and dedicated process to
assign correct codes to each patient admission based on overall diagnosis, we
propose a hierarchical deep learning model with attention mechanism which can
automatically assign ICD diagnostic codes given written diagnosis. We utilize
character-aware neural language models to generate hidden representations of
written diagnosis descriptions and ICD codes, and design an attention mechanism
to address the mismatch between the numbers of descriptions and corresponding
codes. Our experimental results show the strong potential of automated ICD
coding from diagnosis descriptions. Our best model achieves 0.53 and 0.90 of F1
score and area under curve of receiver operating characteristic respectively.
The result outperforms those achieved using character-unaware encoding method
or without attention mechanism. It indicates that our proposed deep learning
model can code automatically in a reasonable way and provide a framework for
computer-auxiliary ICD coding.
