CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning

arXiv:2402.15700v1 Announce Type: new
Abstract: Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized …

abstract arxiv boosting classification clinical code coding cs.ai cs.cl cs.lg diseases extraction information international modeling notes performance recording relations role through type