Structured LLM Augmentation for Clinical Information Extraction | JoVE Visualize

Area of Science:

Clinical informatics
Natural Language Processing (NLP)
Biomedical data science

Background:

Information extraction tasks like Named Entity Recognition (NER) and Relation Extraction (RE) are crucial for clinical research but are limited by scarce labeled clinical data due to privacy and cost.
Existing methods struggle with the unique challenges of clinical text, including privacy concerns and high annotation expenses, hindering progress in extracting valuable insights.

Purpose of the Study:

To develop and evaluate a novel framework that leverages Large Language Models (LLMs) for data augmentation to overcome the scarcity of labeled clinical documents for information extraction tasks.
To enhance the performance of clinical information extraction models by utilizing LLM-generated, contextually accurate, and structurally preserved augmented data.

Main Methods:

A novel framework combining LLMs for data augmentation with an adapted BERT model for clinical information extraction was developed.
The framework encodes entity and relational information within clinical note segments, enabling LLMs to generate diverse augmentations while preserving structural integrity.
A segmentation-based BERT model, incorporating BiLSTM for global context, was trained on the augmented data to overcome sequence length limitations.

Main Results:

The proposed framework demonstrated significant performance improvements on both public and proprietary clinical datasets.
The data augmentation approach effectively addressed the challenge of data scarcity in clinical information extraction.
The segmentation-based BERT model integrated with BiLSTM showed enhanced capabilities in handling clinical text.

Conclusions:

The novel framework effectively addresses data scarcity in clinical information extraction by utilizing LLM-based data augmentation.
The approach shows significant potential for advancing clinical research and applications by improving the accuracy and efficiency of NER and RE tasks.
This method offers a scalable solution for generating high-quality training data in the biomedical domain, overcoming privacy and cost barriers.