  International Classification of Diseases (ICD) coding plays an important role
in systematically classifying morbidity and mortality data. In this study, we
propose a hierarchical label-wise attention Transformer model (HiLAT) for the
explainable prediction of ICD codes from clinical documents. HiLAT firstly
fine-tunes a pretrained Transformer model to represent the tokens of clinical
documents. We subsequently employ a two-level hierarchical label-wise attention
mechanism that creates label-specific document representations. These
representations are in turn used by a feed-forward neural network to predict
whether a specific ICD code is assigned to the input clinical document of
interest. We evaluate HiLAT using hospital discharge summaries and their
corresponding ICD-9 codes from the MIMIC-III database. To investigate the
performance of different types of Transformer models, we develop
ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using
all the MIMIC-III clinical notes. The experiment results show that the F1
scores of the HiLAT+ClinicalplusXLNet outperform the previous state-of-the-art
models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations
of attention weights present a potential explainability tool for checking the
face validity of ICD code predictions.
