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

• URL: http://arxiv.org/abs/2310.07059v2
• Date: Wed, 19 Jun 2024 20:58:52 GMT
• Title: DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
• Authors: Xueren Ge, Satpathy Abhishek, Ronald Dean Williams, John A. Stankovic, Homa Alemzadeh,
• Abstract summary: This paper presents DKEC, Domain Knowledge Enhanced Classification for diagnosis prediction. 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.
• Score: 4.94752151268185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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