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VetLLM:从兽医笔记中预测诊断的大型语言模型.

Yixing Jiang1, Jeremy A Irvin, Andrew Y Ng

  • 1Stanford University, Stanford, CA, United States.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
PubMed
概括

大型语言模型 (LLM) 显示了兽医自动诊断编码的前景. 像VetLLM这样的微调LLM显著提高了准确性,即使数据有限,也超过了传统方法.

科学领域:

  • 兽医信息学 兽医信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 从兽医笔记中编码准确的诊断对于医学和公共卫生研究至关重要.
  • 使用基于规则或监督学习模型的现有方法通常是繁的,缺乏可转移性.

研究的目的:

  • 评估开源大型语言模型 (LLM) 在兽医中用于自动诊断编码的有效性.
  • 通过对兽医临床文本进行微调的LLM来证明可以实现的性能改善.

主要方法:

  • 在兽医编码基准 (CSU和PP) 上评估了Alpaca-7B的零射击性能.
  • 开发和微调一个专门的LLM,VetLLM,在兽医笔记的子集.
  • 将VetLLM的表现与最先进的监督模型进行了比较.

主要成果:

  • 阿尔帕卡-7B实现了0.538 (CSU) 和0.389 (PP) 的零射击F1得分.
  • 在5000纸币上微调的VetLLM获得了0.747 (CSU) 和0.637 (PP) 的F1得分,超过了监督模型.
  • 数据高效微调显示,与100,000多张笔记训练的模型相比,只有200张笔记的模型表现优越.

结论:

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  • 开源的LLM提供了一种可行和有效的方法,用于从兽医笔记中编码诊断.
  • 微调的LLMs为兽医学中的临床文本处理提供了一个强大的,数据效率高的范式.
  • 这种方法具有很大的潜力,可以利用临床数据推进医学和公共卫生研究.