  Medical code assignment from clinical text is a fundamental task in clinical
information system management. As medical notes are typically lengthy and the
medical coding system's code space is large, this task is a long-standing
challenge. Recent work applies deep neural network models to encode the medical
notes and assign medical codes to clinical documents. However, these methods
are still ineffective as they do not fully encode and capture the lengthy and
rich semantic information of medical notes nor explicitly exploit the
interactions between the notes and codes. We propose a novel method, gated
convolutional neural networks, and a note-code interaction (GatedCNN-NCI), for
automatic medical code assignment to overcome these challenges. Our methods
capture the rich semantic information of the lengthy clinical text for better
representation by utilizing embedding injection and gated information
propagation in the medical note encoding module. With a novel note-code
interaction design and a graph message passing mechanism, we explicitly capture
the underlying dependency between notes and codes, enabling effective code
prediction. A weight sharing scheme is further designed to decrease the number
of trainable parameters. Empirical experiments on real-world clinical datasets
show that our proposed model outperforms state-of-the-art models in most cases,
and our model size is on par with light-weighted baselines.
