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High-throughput biomedical relation extraction for semi-structured web articles empowered by large language models.

Songchi Zhou1, Sheng Yu2

  • 1Department of Statistics and Data Science, Tsinghua University, Beijing, China.

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Summary
This summary is machine-generated.

Domain-adapted large language models (LLMs) show superior performance in extracting biomedical relations from semi-structured websites. These models offer a scalable solution for high-throughput biomedical knowledge extraction, benefiting clinical applications.

Keywords:
Artificial intelligenceBiomedical relation extractionLarge language model

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Developing high-throughput biomedical relation extraction systems for semi-structured websites.
  • Leveraging large language models (LLMs) with reading comprehension and medical knowledge.

Purpose of the Study:

  • To create a specialized system for extracting biomedical relations from semi-structured web content.
  • To evaluate the effectiveness of various LLMs, including general-purpose, domain-adapted, and parameter-efficient models.

Main Methods:

  • Relation extraction formulated as binary classification tasks using LLMs.
  • LLMs provide rationales for factual verification of extracted relations.
  • Candidate entities identified via biomedical thesaurus matching; main title as tail entity.

Main Results:

  • Domain-adapted LLMs significantly outperform general-purpose models.
  • MedGemma-27B (F1=0.820) surpasses GPT-4o and GPT-4.1; DeepSeek-V3 achieves the best performance (F1=0.844).
  • Extracted over 225,000 relation triplets across three relation types from authoritative biomedical websites.

Conclusions:

  • LLMs are effective for high-throughput biomedical relation extraction, with domain-adapted models offering practical advantages.
  • The framework is scalable and adaptable for diverse biomedical relations across heterogeneous websites.
  • Extracted relations can enhance knowledge graphs, support evidence-based guidelines, and aid clinical decision-making.