  The medical codes prediction problem from clinical notes has received
substantial interest in the NLP community, and several recent studies have
shown the state-of-the-art (SOTA) code prediction results of full-fledged deep
learning-based methods. However, most previous SOTA works based on deep
learning are still in early stages in terms of providing textual references and
explanations of the predicted codes, despite the fact that this level of
explainability of the prediction outcomes is critical to gaining trust from
professional medical coders. This raises the important question of how well
current explainability methods apply to advanced neural network models such as
transformers to predict correct codes and present references in clinical notes
that support code prediction. First, we present an explainable Read, Attend,
and Code (xRAC) framework and assess two approaches, attention score-based
xRAC-ATTN and model-agnostic knowledge-distillation-based xRAC-KD, through
simplified but thorough human-grounded evaluations with SOTA transformer-based
model, RAC. We find that the supporting evidence text highlighted by xRAC-ATTN
is of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in
production deployment scenarios. More importantly, we show for the first time
that, given the current state of explainability methodologies, using the SOTA
medical codes prediction system still requires the expertise and competencies
of professional coders, even though its prediction accuracy is superior to that
of human coders. This, we believe, is a very meaningful step toward developing
explainable and accurate machine learning systems for fully autonomous medical
code prediction from clinical notes.
