Epidemic Information Extraction for Event-Based Surveillance using Large Language Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909295906390016 |
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| author | Consoli, Sergio Markov, Peter Stilianakis, Nikolaos I. Bertolini, Lorenzo Gallardo, Antonio Puertas Ceresa, Mario |
| author_facet | Consoli, Sergio Markov, Peter Stilianakis, Nikolaos I. Bertolini, Lorenzo Gallardo, Antonio Puertas Ceresa, Mario |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Epidemic Information Extraction for Event-Based Surveillance using Large Language Models Consoli, Sergio Markov, Peter Stilianakis, Nikolaos I. Bertolini, Lorenzo Gallardo, Antonio Puertas Ceresa, Mario Computational Engineering, Finance, and Science Computation and Language 68T01, 68T50 I.2; I.2.7; I.2.6 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. |
| title | Epidemic Information Extraction for Event-Based Surveillance using Large Language Models |
| topic | Computational Engineering, Finance, and Science Computation and Language 68T01, 68T50 I.2; I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2408.14277 |