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Scaling Sensor Metadata Extraction for Exposure Health Using LLMs.

Fatemeh Shah-Mohammadi, Sunho Im, Julio C Facelli

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    Summary
    This summary is machine-generated.

    This study introduces a large language model (LLM) pipeline to automate sensor metadata extraction from research literature, improving efficiency and accuracy for exposome and exposure health research.

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    Area of Science:

    • Environmental Health Sciences
    • Computational Biology
    • Data Science

    Background:

    • Diverse sensor technologies and inconsistent metadata reporting hinder exposome and exposure health research.
    • Manual extraction of sensor metadata from unstructured literature is a significant bottleneck.

    Purpose of the Study:

    • To develop and evaluate a large language model (LLM)-based pipeline for automated sensor metadata extraction and harmonization.
    • To address the scalability and efficiency challenges in processing exposure health literature.

    Main Methods:

    • Utilized GPT-4 in a zero-shot setting to parse full-text PDFs.
    • Developed a pipeline to extract sensor metadata and harmonize it into structured formats.

    Main Results:

    • The automated pipeline significantly increased extraction speed compared to manual review.
    • Achieved high performance with 94.74% average accuracy/precision, 100% recall, and 97.28% F1-score.

    Conclusions:

    • LLMs offer a feasible and scalable solution for automating sensor metadata extraction in exposure health.
    • This approach reduces manual effort and enhances metadata completeness and consistency for informatics platforms.