  Multi-label text classification (MLTC) tasks in the medical domain often face
the long-tail label distribution problem. Prior works have explored
hierarchical label structures to find relevant information for few-shot
classes, but mostly neglected to incorporate external knowledge from medical
guidelines. This paper presents DKEC, Domain Knowledge Enhanced Classification
for diagnosis prediction with two innovations: (1) automated construction of
heterogeneous knowledge graphs from external sources to capture semantic
relations among diverse medical entities, (2) incorporating the heterogeneous
knowledge graphs in few-shot classification using a label-wise attention
mechanism. We construct DKEC using three online medical knowledge sources and
evaluate it on a real-world Emergency Medical Services (EMS) dataset and a
public electronic health record (EHR) dataset. Results show that DKEC
outperforms the state-of-the-art label-wise attention networks and transformer
models of different sizes, particularly for the few-shot classes. More
importantly, it helps the smaller language models achieve comparable
performance to large language models.
