Structured LLM Augmentation for Clinical Information Extraction
Abstract
Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to privacy concerns and high annotation costs. This study introduces a novel framework combining Large Language Models (LLMs) for data augmentation with an adapted BERT model for clinical information extraction. The framework encodes entity and relational information within clinical note segments, enabling LLMs to generate diverse and contextually accurate augmentations while preserving structural integrity. Augmented data is used to train a segmentation-based BERT model, overcoming sequence length limitations and integrating global context via BiLSTM. Evaluations on public and proprietary datasets demonstrate significant performance improvements, highlighting the approach's potential to address data scarcity in clinical information extraction tasks.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.