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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Automated Extraction of Mortality Information From Publicly Available Sources Using Large Language Models:

Mohammed Al-Garadi1, Michele LeNoue-Newton1, Michael E Matheny1

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN, 37203, United States, 1 2139151696.

Journal of Medical Internet Research
|August 18, 2025
PubMed
Summary

New NLP and LLM methods extract mortality data from online sources, improving surveillance. These tools enhance timeliness and completeness for public health and medical product safety.

Keywords:
LLMNLPautomated surveillancefew-shot learninghealth care analyticslarge language modelmortality datanatural language processingobituariespublic health informaticssocial media analysis

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Area of Science:

  • Health Informatics
  • Natural Language Processing (NLP)
  • Public Health Surveillance

Background:

  • Traditional mortality data sources (e.g., National Death Index, EHRs) face challenges with data lags, missing information, and incomplete coverage.
  • Publicly available digital content from social media, crowdfunding, and memorials presents a potential supplementary data source for mortality surveillance.
  • Existing tools for extracting mortality information from unstructured online data are underdeveloped.

Purpose of the Study:

  • To develop scalable Natural Language Processing (NLP) and Large Language Model (LLM) approaches for extracting mortality information from diverse web-based data sources.
  • To evaluate the performance of these NLP and LLM methods across various online platforms, including social media, crowdfunding sites, and web-based obituaries.

Main Methods:

  • Collected US-based mortality-relevant data from X (formerly Twitter), GoFundMe, EverLoved, TributeArchive, and web obituaries (2015-2022).
  • Developed a transformer-based NLP pipeline to extract decedent names, dates of birth, and dates of death.
  • Employed a few-shot learning (FSL) approach with LLMs to identify primary and secondary causes of death (CoD), with performance assessed against human-annotated or adjudicated data.

Main Results:

  • The best-performing model achieved a microaveraged F1-score of 0.88 for mortality information extraction.
  • The FSL-LLM approach demonstrated high accuracy in identifying primary CoD, reaching 95.9% on GoFundMe, 96.5% on obituaries, and 98% on memorial websites.
  • Model performance closely approached that of human annotators/adjudicators across different data sources.

Conclusions:

  • Advanced NLP and LLM techniques are feasible for extracting mortality data from public web sources, enhancing surveillance timeliness and completeness.
  • These digital data sources offer a valuable complement to traditional systems, potentially improving public health monitoring and medical product safety assessments.
  • Further validation and integration into national surveillance systems are recommended to leverage these findings in real-world healthcare settings.