  Clinical diagnosis, which aims to assign diagnosis codes for a patient based
on the clinical note, plays an essential role in clinical decision-making.
Considering that manual diagnosis could be error-prone and time-consuming, many
intelligent approaches based on clinical text mining have been proposed to
perform automatic diagnosis. However, these methods may not achieve
satisfactory results due to the following challenges. First, most of the
diagnosis codes are rare, and the distribution is extremely unbalanced. Second,
existing methods are challenging to capture the correlation between diagnosis
codes. Third, the lengthy clinical note leads to the excessive dispersion of
key information related to codes. To tackle these challenges, we propose a
novel framework to combine the inheritance-guided hierarchical assignment and
co-occurrence graph propagation for clinical automatic diagnosis. Specifically,
we propose a hierarchical joint prediction strategy to address the challenge of
unbalanced codes distribution. Then, we utilize graph convolutional neural
networks to obtain the correlation and semantic representations of medical
ontology. Furthermore, we introduce multi attention mechanisms to extract
crucial information. Finally, extensive experiments on MIMIC-III dataset
clearly validate the effectiveness of our method.
