Effective Medical Code Prediction via Label Internal Alignment
Guodong Liu
The clinical notes are usually typed into the system by physicians. They are
typically required to be marked by standard medical codes, and each code
represents a diagnosis or medical treatment procedure. Annotating these notes
is time consuming and prone to error. In this paper, we proposed a multi-view
attention based Neural network to predict medical codes from clinical texts.
Our method incorporates three aspects of information, the semantic context of
the clinical text, the relationship among the label (medical codes) space, and
the alignment between each pair of a clinical text and medical code. Our method
is verified to be effective on the open source dataset. The experimental result
shows that our method achieves better performance against the prior
state-of-art on multiple metrics.