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Biomedical text readability after hypernym substitution with fine-tuned large language models.

Karl Swanson1, Shuhan He2, Josh Calvano3

  • 1Department of Medicine-Clinical Informatics, University of California-San Francisco, San Francisco, United States of America.

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Summary

Large language models (LLMs) simplify complex biomedical text by replacing difficult terms with simpler synonyms. This improves patient understanding of health information, making medical content more accessible.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Health Literacy

Background:

  • Patient access to online medical information necessitates text simplification.
  • Biomedical text comprehension is challenging due to specialized vocabulary.
  • Improving patient understanding is crucial for health engagement.

Purpose of the Study:

  • To investigate the simplification of biomedical text using large language models (LLMs).
  • To assess the effectiveness of hypernym substitution for enhancing text readability.
  • To evaluate different LLM variants for biomedical text processing.

Main Methods:

  • Finetuned three LLM variants for complex word/phrase substitution with hypernyms.
  • Processed 1,000 biomedical definitions from the Unified Medical Language System (UMLS).
  • Evaluated text changes using readability metrics and sentence complexity measures.

Main Results:

  • All three LLM approaches improved biomedical text readability and reduced sentence complexity.
  • Readability shifted from collegiate to US high-school level.
  • GPT-J-6b demonstrated the most significant improvement in sentence complexity.

Conclusions:

  • Hypernym substitution is a viable method for simplifying complex biomedical text.
  • Fine-tuning open-access LLMs is effective for biomedical NLP tasks.
  • Simplified medical information enhances public health literacy and engagement.