A Two-Stage Decoder for Efficient ICD Coding
This work addresses the challenging multilabel classification task of ICD coding for healthcare applications, but it is incremental as it builds on existing hierarchical methods.
The paper tackles the problem of automated ICD coding from clinical notes by proposing a two-stage decoder that mimics human coders' hierarchical approach, achieving competitive performance on the MIMIC-III dataset without external data.
Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures. ICD coding is a challenging multilabel text classification problem due to noisy clinical document inputs and long-tailed label distribution. Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. However, most of them do not reflect how human coders generate the code: first, the coders select general code categories and then look for specific subcategories that are relevant to a patient's condition. Inspired by this, we propose a two-stage decoding mechanism to predict ICD codes. Our model uses the hierarchical properties of the codes to split the prediction into two steps: At first, we predict the parent code and then predict the child code based on the previous prediction. Experiments on the public MIMIC-III data set show that our model performs well in single-model settings without external data or knowledge.