SMP-Graph: Structure-Enhanced Unsupervised Semantic Graph Representation for Precise Medical Procedure Coding on EMRs

Yue Gao, Xiangling Fu, Xien Liu, Kaiyin Zhou, Ji Wu

Abstract: Automatic ICD coding, as a fundamental task in the field of healthcare management, has been paid much attention by researchers. However, the current deep learning-based ICD coding research mostly focus on the introduction of external diagnostic description text or the imposition of rules, while ignoring the structured features of the coding text itself. Especially for short texts of medical procedure codes, it is much more important to mine the information value in the texts. In this paper, we propose a structure-enhanced unsupervised semantic graph representation for precise medical procedure coding (SMP-Graph). The SMP-Graph representation method constructs each medical procedure text with an inductive heterogeneous graph and particularly enhances the kernel knowledge by extracting the axis words and chapter title and allocating them with distinct node representations. Both the nodes and edges are generated by the unsupervised pretrained model and then interact information in a bidirectionally weighted graph structure. Therefore, the SMP-Graph really realizes the intra-integration of unsupervised contextualized information and graph-based global information from the medical procedure code. Experiments conducted on the Chinese ICD-9-CM-3 procedure text dataset we collected from EMRs demonstrate that the SMP-Graph is a better representation method that outperforms other representative methods for medical procedure coding. Characteristic analysis is also conducted to prove the interpretability and adaptability of the SMP-Graph on the medical procedure coding task.