Epidemic Information Extraction for Event-Based Surveillance using Large Language Models
<|reference_start|>Epidemic Information Extraction for Event-Based Surveillance using Large Language Models: 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.<|reference_end|>
@article{consoli2024epidemic,
    title={Epidemic Information Extraction for Event-Based Surveillance using Large
  Language Models},
    author={Sergio Consoli, Peter Markov, Nikolaos I. Stilianakis, Lorenzo
  Bertolini, Antonio Puertas Gallardo, Mario Ceresa},
    journal={Lecture Notes in Networks and Systems, 2024, vol 1011, pages
  241-252. Springer, Singapore},
    year={2024},
    doi={10.1007/978-981-97-4581-4_17},
    archivePrefix={arXiv},
    eprint={2408.14277},
    primaryClass={cs.CE cs.CL}
}