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<p>Clinical management of Alzheimer disease (AD) leverages information in unstructured narratives. We compared large language models, GPT-4, Phi-4-14b, DeepSeek-R1-Distill-LLaMA-8b, and LLaMA-3.2-3b, against a previous rule-based pipeline for extracting AD-related phenotypes from 100 real-world clinical notes. Evaluated against clinician annotations, GPT-4 performed best across phenotypes (median precision = 0.96, recall = 1, F1 = 0.98), but rule-based natural language processing was most precise (median = 0.97). Model choice depends on context, considering trade-offs between precision, recall, and scalability.</p>

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Comparing Alzheimer disease phenotype extraction using rule-based natural language processing, GPT-4, Phi-4, LLaMA, and DeepSeek

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Inez Y. Oh,
  • William J. B. Powell,
  • Suzanne E. Schindler,
  • Nupur Ghoshal,
  • Albert M. Lai,
  • Philip R. O. Payne,
  • Aditi Gupta

摘要

Clinical management of Alzheimer disease (AD) leverages information in unstructured narratives. We compared large language models, GPT-4, Phi-4-14b, DeepSeek-R1-Distill-LLaMA-8b, and LLaMA-3.2-3b, against a previous rule-based pipeline for extracting AD-related phenotypes from 100 real-world clinical notes. Evaluated against clinician annotations, GPT-4 performed best across phenotypes (median precision = 0.96, recall = 1, F1 = 0.98), but rule-based natural language processing was most precise (median = 0.97). Model choice depends on context, considering trade-offs between precision, recall, and scalability.