Multi-label text classification (MLTC) tasks in the medical domain often face
long-tail label distribution, where rare classes have fewer training samples
than frequent classes. Although previous works have explored different model
architectures and hierarchical label structures to find important features,
most of them neglect to incorporate the domain knowledge from medical
guidelines. In this paper, we present DKEC, Domain Knowledge Enhanced
Classifier for medical diagnosis prediction with two innovations: (1) a
label-wise attention mechanism that incorporates a heterogeneous graph and
domain ontologies to capture the semantic relationships between medical
entities, (2) a simple yet effective group-wise training method based on
similarity of labels to increase samples of rare classes. We evaluate DKEC on
two real-world medical datasets: the RAA dataset, a collection of 4,417 patient
care reports from emergency medical services (EMS) incidents, and a subset of
53,898 reports from the MIMIC-III dataset. Experimental results show that our
method outperforms the state-of-the-art, particularly for the few-shot (tail)
classes. More importantly, we study the applicability of DKEC to different
language models and show that DKEC can help the smaller language models achieve
comparable performance to large language models.

通过融合领域知识与文本特征，DKEC 算法以标签关注机制为基础，在医学诊断预测领域取得卓越表现，尤其对于少样本类别具有显著优势，并有助于小型语言模型取得与大型语言模型相当的性能。