Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding
TL;DR
A deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention followed by label-wise attention is proposed for medical coding, achieving significantly better F-1 scores and a remarkable increase in recall score.
Abstract
Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts of the text and focus to different sections for each individual code, making it a very difficult problem even for professional human coders. We model the task as a multi-label text classification problem. To overcome the mentioned difficulties, we propose a deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention. While multi-layer TCN helps extract a global document representation with the ability to learn relations over very long sequences, label-specific attention mechanism allows the model to focus on different aspects of the same document for each individual label. Our method achieves significantly better F-1 scores (9% increase) compared to the previous state-of-the-art model, with a remarkable increase in recall score (28% increase), which we believe is the more important metric for a clinical decision support setting.