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利用开源大型语言模型在医院工作人员调查中进行数据增强:混合方法研究

Carl Ehrett1, Sudeep Hegde2, Kwame Andre3

  • 1Watt Family Innovation Center, Clemson University, Clemson, SC, United States.

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概括

开源大型语言模型 (LLM) 可以有效地增强用于文本分类的小型医疗数据集. 这种方法提高了分类器的性能,为医学教育和患者护理提供了隐私意识的解决方案.

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在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.数据增强数据增强数据隐私 隐私数据 隐私数据数据安全数据安全伦理学 伦理 伦理学大型语言模型.医学教育 医学教育医务人员 医疗人员 医疗人员自然语言处理自然语言处理.

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科学领域:

  • 医疗保健中的人工智能
  • 自然语言处理自然语言处理.
  • 医疗教育 技术 技术 医学教育

背景情况:

  • 生成型大语言模型 (LLM) 显示了医学教育的潜力,但它们在医疗保健中用于增强小型数据集的应用,特别是隐私和成本限制,尚未得到充分探索.
  • 现有的LLM应用程序通常依赖于第三方服务,限制其在敏感医疗保健环境中的使用.

研究的目的:

  • 调查开源LLM在医疗保健中的文本分类任务中数据增强方面的有效性.
  • 评估大型语言模型MetaAI (LLaMA) 和Alpaca等模型的性能,用于为医院工作人员调查生成合成数据.

主要方法:

  • 采用了涉及数据增强和文本分类的两步过程.
  • 四个开源生成的LLM被用来创建有关COVID-19流行病适应性的医院工作人员调查的合成数据.
  • 然后使用三个不同的分类器LLM来对增强文本数据进行分类.

主要成果:

  • 最好的性能是使用LLaMA 7B (温度0.7,100增强) 来增强数据和使用强大优化的BERT预训练方法 (RoBERTa) 来进行分类,平均AUC为0.87.
  • 开源的LLM在有限的医疗保健数据集上显著提高了文本分类器的性能.

结论:

  • 开源LLM为医疗保健环境中的数据增强提供了可行的解决方案,提高了文本分类的准确性.
  • 该研究强调了在医疗应用中实施LLM时隐私和伦理考虑的重要性.
  • 未来的研究应该探索LLMs在医学教育和患者护理中的进一步应用和优化.