  Medical coding translates professionally written medical reports into
standardized codes, which is an essential part of medical information systems
and health insurance reimbursement. Manual coding by trained human coders is
time-consuming and error-prone. Thus, automated coding algorithms have been
developed, building especially on the recent advances in machine learning and
deep neural networks. To solve the challenges of encoding lengthy and noisy
clinical documents and capturing code associations, we propose a multitask
recalibrated aggregation network. In particular, multitask learning shares
information across different coding schemes and captures the dependencies
between different medical codes. Feature recalibration and aggregation in
shared modules enhance representation learning for lengthy notes. Experiments
with a real-world MIMIC-III dataset show significantly improved predictive
performance.
