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Related Experiment Video

Updated: Apr 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies For

Masood Sujau1, Masako Wada1, Emilie Vallée1

  • 1School of Veterinary Science, Massey University, Palmerston North 4442, New Zealand.

Machine Learning and Knowledge Extraction
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

A novel large language model (LLM) approach significantly reduces manual screening effort for zoonotic disease research by over 70%. This AI-powered method aids in extracting crucial data for disease spread models, improving climate-sensitive outbreak preparedness.

Keywords:
AI-assisted disease surveillanceautomated AI literature screeningbiomedical text mining for disease trackingclimate-sensitive zoonotic disease modellinginformation retrieval in medical literaturelarge language models in systematic reviewssystematic literature review automationzero-shot relevancy ranking

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Area of Science:

  • Environmental science and public health
  • Computational biology and bioinformatics
  • Artificial intelligence in scientific research

Background:

  • Climate change and human activities increase zoonotic disease emergence risk.
  • Accurate disease spread models require data from scientific literature.
  • Manual literature review for data extraction is time-consuming and error-prone.

Purpose of the Study:

  • To evaluate a large language model (LLM) for screening climate-sensitive zoonotic disease research articles.
  • To assess the efficiency and accuracy of LLM-based screening compared to manual methods.
  • To explore the potential of AI in automating data extraction for disease modeling.

Main Methods:

  • Framing article selection as a question-answer task using zero-shot chain-of-thought prompting.
  • Utilizing LLMs to screen research papers for relevance to zoonotic diseases and climate variables.
  • Validating the LLM approach across four datasets covering distinct zoonotic diseases and rainfall data.

Main Results:

  • The LLM achieved at least a 70% reduction in work effort compared to manual screening at 95% recall (NWSS@95%).
  • The method demonstrated effectiveness across multiple zoonotic diseases and climate variables.
  • The LLM provided explainable AI rationales for article ranking.

Conclusions:

  • LLMs can significantly accelerate systematic literature reviews in climate-sensitive zoonotic disease research.
  • The proposed AI approach offers a scalable and efficient solution for data extraction.
  • This advancement supports improved parametrization of disease spread models and pandemic preparedness.