Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study

This study demonstrates the feasibility of using advanced NLP and LLM techniques to extract mortality data from publicly available web-based sources. These methods can significantly enhance the timeliness, completeness, and granularity of mortality surveillance, offering a valuable complement to traditional data systems. By enabling earlier detection of mortality signals and improving CoD classification across large populations, this approach may support more responsive public health monitoring and medical product safety assessments. Further work is needed to validate these findings in real-world health care settings and facilitate the integration of digital data sources into national public health surveillance systems.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-08-18
DOI
https://doi.org/10.2196/71113
Citations
3
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
4.06
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article

Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study

Mohammed Ali Al-Garadi, Rishi Desai, Michael E. Matheny, Michele L. Lenoue-Newton et al.
3 citations
Journal of Medical Internet Research
Data-Driven Disease Surveillance
4.06
article

Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models: Development and Evaluation Study

Mohammed Ali Al-Garadi, Rishi Desai, Michael E. Matheny, Michele L. Lenoue-Newton, Mirza S. Khan, Dax Westerman, Ruth Reeves, Aida Kuzucan, Melissa L McPheeters, José J. Hernández‐Muñoz, Michael F McLemore, Jeff Deere, Xi Wang, Jill M Whitaker
article en
3 citations

Abstract

This study demonstrates the feasibility of using advanced NLP and LLM techniques to extract mortality data from publicly available web-based sources. These methods can significantly enhance the timeliness, completeness, and granularity of mortality surveillance, offering a valuable complement to traditional data systems. By enabling earlier detection of mortality signals and improving CoD classification across large populations, this approach may support more responsive public health monitoring and medical product safety assessments. Further work is needed to validate these findings in real-world health care settings and facilitate the integration of digital data sources into national public health surveillance systems.

Journal of Medical Internet ResearchVol. 27
United States Food and Drug Administration (US), Harvard University Press (US), Triangle (US), Vanderbilt University Medical Center (US)
Good health and well-being
Openalex Percentile: Top 6%
Data-Driven Disease Surveillance
4.06
DOIOpen Access PDFOpenAlex
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