  International Classification of Diseases (ICD) is a global medical
classification system which provides unique codes for diagnoses and procedures
appropriate to a patient's clinical record. However, manual coding by human
coders is expensive and error-prone. Automatic ICD coding has the potential to
solve this problem. With the advancement of deep learning technologies, many
deep learning-based methods for automatic ICD coding are being developed. In
particular, a label attention mechanism is effective for multi-label
classification, i.e., the ICD coding. It effectively obtains the label-specific
representations from the input clinical records. However, because the existing
label attention mechanism finds key tokens in the entire text at once, the
important information dispersed in each paragraph may be omitted from the
attention map. To overcome this, we propose a novel neural network architecture
composed of two parts of encoders and two kinds of label attention layers. The
input text is segmentally encoded in the former encoder and integrated by the
follower. Then, the conventional and partition-based label attention mechanisms
extract important global and local feature representations. Our classifier
effectively integrates them to enhance the ICD coding performance. We verified
the proposed method using the MIMIC-III, a benchmark dataset of the ICD coding.
Our results show that our network improves the ICD coding performance based on
the partition-based mechanism.
