DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction

Xueren Ge; Satpathy Abhishek; Ronald Dean Williams; John A. Stankovic; Homa Alemzadeh

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arXiv · 2310.07059

DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction

Xueren Ge·Satpathy Abhishek·Ronald Dean Williams·John A. Stankovic·Homa Alemzadeh

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Abstract

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.

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Xueren Ge, Satpathy Abhishek, Ronald Dean Williams, John A. Stankovic, Homa Alemzadeh. 2024-06-19. DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction. https://arxiv.org/abs/2310.07059

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