Leveraging Open-Source Large Language Models for Data Augmentation in Hospital Staff Surveys: Mixed Methods Study.

Area of Science:

• Artificial Intelligence in Healthcare
• Natural Language Processing
• Medical Education Technology

Background:

• Generative large language models (LLMs) show potential for medical education but their use in healthcare for augmenting small datasets, especially with privacy and cost constraints, is underexplored.
• Existing LLM applications often rely on third-party services, limiting their use in sensitive healthcare contexts.

Purpose of the Study:

• To investigate the efficacy of open-source LLMs for data augmentation in text classification tasks within healthcare.
• To evaluate the performance of models like Large Language Model Meta AI (LLaMA) and Alpaca for generating synthetic data for hospital staff surveys.

Main Methods:

• A two-step process involving data augmentation and text classification was employed.
• Four open-source generative LLMs were used to create synthetic data from hospital staff surveys concerning COVID-19 pandemic adaptations.
• Three distinct classifier LLMs were then used to categorize the augmented text data.

Main Results:

• The best performance was achieved using LLaMA 7B (temperature 0.7, 100 augments) for data augmentation and Robustly Optimized BERT Pretraining Approach (RoBERTa) for classification, yielding an average AUC of 0.87.
• Open-source LLMs significantly improved text classifier performance on limited healthcare datasets.

Conclusions:

• Open-source LLMs offer a viable solution for data augmentation in healthcare settings, enhancing text classification accuracy.
• The study underscores the importance of privacy and ethical considerations when implementing LLMs in medical applications.
• Future research should explore further applications and optimizations of LLMs in medical education and patient care.