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Information extraction from medical case reports using OpenAI InstructGPT.

Veronica Sciannameo1, Daniele Jahier Pagliari2, Sara Urru3

  • 1Centre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, Orbassano 10043, Italy.

Computer Methods and Programs in Biomedicine
|July 19, 2024
PubMed
Summary

Large Language Models (LLMs) like InstructGPT show high accuracy in extracting patient data from medical case reports. This technology offers a promising, no-code solution for clinical information retrieval from unstructured text.

Keywords:
Case reportsInformation retrievalLarge language modelNatural language processing

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Automated solutions like Natural Language Processing (NLP) struggle with clinical text's complexity.
  • Large Language Models (LLMs) offer a potential solution for unstructured clinical data.
  • InstructGPT, derived from GPT-3, was evaluated for clinical information extraction.

Purpose of the Study:

  • To evaluate InstructGPT's performance in extracting patient information from medical case reports.
  • To compare the effectiveness of LLMs against traditional NLP methods for clinical data extraction.

Main Methods:

  • Searched PubMed, Scopus, and Web of Science for 208 case reports on pediatric foreign body injuries.
  • Manually extracted patient data (sex, age, object, injured body part) to create a gold standard.
  • Compared InstructGPT's extraction accuracy against the gold standard.

Main Results:

  • InstructGPT achieved high accuracy: 94% for sex, 82% for age, 94% for object, and 89% for body part.
  • Excluding unretrievable articles improved accuracy to 97% for sex/age and 93% for body part.
  • InstructGPT successfully extracted information from non-English articles.

Conclusions:

  • LLMs enable zero-shot extraction of clinical information from unstructured text, like case reports.
  • No pre-processing or technical NLP/ML expertise is required for PDF-based extraction.
  • The study demonstrates LLMs' potential for efficient clinical data retrieval from diverse scientific literature.