CoRelation: Boosting Automatic ICD Coding Through Contextualized Code
  Relation Learning
Junyu Luo Xiaochen Wang Jiaqi Wang Aofei Chang Yaqing Wang Fenglong Ma
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 and flexible
framework, to enhance the learning of ICD code representations. Our approach,
unlike existing methods, employs a dependent learning paradigm that considers
the context of clinical notes in modeling all possible code relations. We
evaluate our approach on six public ICD coding datasets and the experimental
results demonstrate the effectiveness of our approach compared to
state-of-the-art baselines.