  ICD coding is a process of assigning the International Classification of
Disease diagnosis codes to clinical/medical notes documented by health
professionals (e.g. clinicians). This process requires significant human
resources, and thus is costly and prone to error. To handle the problem,
machine learning has been utilized for automatic ICD coding. Previous
state-of-the-art models were based on convolutional neural networks, using a
single/several fixed window sizes. However, the lengths and interdependence
between text fragments related to ICD codes in clinical text vary
significantly, leading to the difficulty of deciding what the best window sizes
are. In this paper, we propose a new label attention model for automatic ICD
coding, which can handle both the various lengths and the interdependence of
the ICD code related text fragments. Furthermore, as the majority of ICD codes
are not frequently used, leading to the extremely imbalanced data issue, we
additionally propose a hierarchical joint learning mechanism extending our
label attention model to handle the issue, using the hierarchical relationships
among the codes. Our label attention model achieves new state-of-the-art
results on three benchmark MIMIC datasets, and the joint learning mechanism
helps improve the performances for infrequent codes.
