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

Published in The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP main conference 2024), 2024

Recommended citation: @inproceedings{ge-etal-2024-dkec, title = "{DKEC}: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction", author = "Ge, Xueren and Satpathy, Abhishek and Williams, Ronald Dean and Stankovic, John and Alemzadeh, Homa", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.712", pages = "12798--12813", 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.", }