Effective Medical Code Prediction via Label Internal Alignment
This work addresses the challenge of automating medical code annotation for healthcare professionals, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of predicting medical codes from clinical notes, which is time-consuming and error-prone when done manually, and reports that their multi-view attention neural network achieves better performance than prior state-of-the-art methods on multiple metrics.
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.