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Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.

Min-Soo Kim1,2, Philip Chung2, Nima Aghaeepour2

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Summary

Generative Pre-trained Transformer (GPT)-3.5 and GPT-4 show varying accuracy in clinical text extraction. Enhancing prompts with definitions improves GPT-4 performance for complex data extraction.

Keywords:
Access to InformationMedical Informatics.Medical RecordsNatural Language Processing

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Area of Science:

  • Natural Language Processing in Healthcare
  • Artificial Intelligence in Clinical Research
  • Biomedical Informatics

Background:

  • Clinical text analysis is complex due to its unstructured nature.
  • Large Language Models (LLMs) like GPT offer potential for information extraction.
  • Comparing different GPT versions is crucial for optimizing clinical data processing.

Purpose of the Study:

  • To compare the information extraction performance of GPT-3.5 and GPT-4 on clinical texts.
  • To evaluate the impact of prompt engineering on LLM accuracy for clinical data.
  • To identify optimal strategies for using LLMs in clinical text analysis.

Main Methods:

  • Utilized three types of clinical texts (patient characteristics, medical history, test results) from case reports.
  • Applied simple prompts and Greedy Approach decoding strategy for information extraction.
  • Implemented alternative decoding strategies and task-specific definitions for prompt enhancement.

Main Results:

  • Both GPT models extracted straightforward information accurately with simple prompts.
  • GPT-4 achieved higher accuracy for sex extraction (95%) than GPT-3.5 (70%).
  • GPT-3.5 outperformed GPT-4 in BMI extraction (78% vs. 57%); prompt refinement improved GPT-4's accuracy.

Conclusions:

  • GPT models are adequate for simple clinical text extraction tasks.
  • Task-specific definitions in prompts are more effective than simple prompts for complex extractions.
  • Expert prompt design and outcome monitoring are essential for reliable LLM use in clinical settings.