Graph-Augmented Transformer for Clinical Notes (GAT-CN): A Graph Neural Network Approach for Symptom Detection in Clinical Notes After Chemotherapy Initiation

Presentation Time: 02:00 PM - 02:12 PM

Abstract Keywords: Machine Learning, Deep Learning, Large Language Models (LLMs), Bioinformatics, Information Extraction, Fairness and elimination of bias
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

This study introduces GAT-CN, a novel NLP approach that integrates transformer models with graph neural networks to identify post-chemotherapy symptoms associated with acute care utilization from clinical notes. This graph structure enables the model to learn patient node embeddings that encapsulate both the content of individual notes and the broader symptom context. GAT-CN outperforms state-of-the-art models, demonstrating its potential to augment existing transformer models and contributing to improved care for oncology patients.

Speaker(s):
Behzad Naderalvojoud, PhD
Stanford University