Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022)

Abstract: The International Classification of Diseases (ICD) is the foundation of global health statistics and epidemiology. The ICD is designed to translate health conditions into alphanumeric codes. A number of approaches have been proposed for automatic ICD coding, since manual coding is labor-intensive and there is a global shortage of healthcare workers. However, existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment. In this paper, we exploit the discourse structure by leveraging section type classification and section type embeddings. We also focus on the class-imbalanced problem and the heterogeneous writing style between clinical notes and ICD code definitions. The proposed reconciled embedding approach is able to tackle them simultaneously. Experimental results on the MIMIC dataset show that our model outperforms all previous state-of-the-art models by a large margin. The source code is available at https://github.com/discnet2022/discnet

## introduction the international classification of diseases (icd) is a classification system maintained by the world health organization. the system is designed to map health conditions to pre-defined icd codes, allowing the world to share healthcare data in a consistent way between different regions. the foundation of global health statistics and epidemiology is based on the icd. the icd coding task (as shown in ## method we propose a novel neural architecture for automatic icd coding given unstructured discharge summaries. a discharge summary from an electronic health record (ehr) is an unstructured clinical document that outlines the details of a hospital stay. we partition clinical documents into sections and exploit the discourse structure by leveraging section type embeddings. the icd code representations are learned using a reconciled embedding approach. finally, we use a dot production to predict the codes.