Epidemic Information Extraction for Event-Based Surveillance using Large Language Models
This work addresses the need for more effective epidemic surveillance tools for public health officials, though it appears incremental as it applies existing LLM techniques to a specific domain.
The paper tackled the problem of extracting epidemic information from unstructured data sources like ProMED and WHO Disease Outbreak News using Large Language Models (LLMs), finding that LLMs, enhanced with in-context learning and ensemble methods, can significantly improve accuracy and timeliness in epidemic modeling and forecasting.
This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) for effective interpretation of unstructured big data sources, like the popular ProMED and WHO Disease Outbreak News. We explore several LLMs, evaluating their capabilities in extracting valuable epidemic information. We further enhance the capabilities of the LLMs using in-context learning, and test the performance of an ensemble model incorporating multiple open-source LLMs. The findings indicate that LLMs can significantly enhance the accuracy and timeliness of epidemic modelling and forecasting, offering a promising tool for managing future pandemic events.