Description-based Label Attention Classifier for Explainable ICD-9 Classification

Abstract

ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN-and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes. We evaluate our proposed method with different transformer-based encoders on the MIMIC-III-50 dataset. Our method achieves strong results together with augmented explainablilty.

Medical Diagnosis Coding Automation: Similarity Search vs. Generative AI

“…BiCapsNetLE [44] integrates ICD code descriptions into word embeddings of clinical notes, enhancing alignment. DLAC [45] employs a description-based label attention mechanism, focusing on the correlation between the descriptions of ICD codes and the features of medical notes. ICDBigBird uses a Graph Convolutional Network (GCN) and ehances the ICD code emebddings by using their relational structure.…”