  Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually
done manually in hospitals but could potentially be automated to improve the
efficiency and accuracy of medical coding. Recent studies on deep learning for
automated medical coding achieved promising performances. However, the
explainability of these models is usually poor, preventing them to be used
confidently in supporting clinical practice. Another limitation is that these
models mostly assume independence among labels, ignoring the complex
correlation among medical codes which can potentially be exploited to improve
the performance. We propose a Hierarchical Label-wise Attention Network (HLAN),
which aimed to interpret the model by quantifying importance (as attention
weights) of words and sentences related to each of the labels. Secondly, we
propose to enhance the major deep learning models with a label embedding (LE)
initialisation approach, which learns a dense, continuous vector representation
and then injects the representation into the final layers and the label-wise
attention layers in the models. We evaluated the methods using three settings
on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS
COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE
initialisation to the state-of-the-art neural network based methods. HLAN
achieved the best Micro-level AUC and $F_1$ on the top-50 code prediction and
comparable results on the NHS COVID-19 shielding code prediction to other
models. By highlighting the most salient words and sentences for each label,
HLAN showed more meaningful and comprehensive model interpretation compared to
its downgraded baselines and the CNN-based models. LE initialisation
consistently boosted most deep learning models for automated medical coding.

Computation and Language;Machine Learning