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通过使用大型语言模型总结癌症病理学报告进行综合报告.

Sivaraman Rajaganapathy, Shaika Chowdhury, Vincent Buchner

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    概括

    大型语言模型 (LLM) 现在可以从叙事病理报告中自动生成结构化的综合报告. 这一创新有望通过提高报告准确性和效率来改善患者护理.

    科学领域:

    • 医疗信息学 医疗信息学
    • 自然语言处理自然语言处理.
    • 医疗保健中的人工智能

    背景情况:

    • 综合报告结构临床信息,通过减少错误和提高报告完整性来改善患者护理.
    • 从叙事数据中手动合成综合报告是劳动密集型的,容易出现错误,特别是许多数据字段.
    • 大型语言模型 (LLM) 提供了先进的自然语言处理能力,在医疗应用中具有尚未开发的潜力.

    研究的目的:

    • 探索最先进的LLM用于自动生成综合性报告的应用.
    • 评估使用LLM在合成结构化临床信息中的有效性和挑战.

    主要方法:

    • 利用了7774份叙述病理学报告的数据集,并使用了相应的注释综合报告.
    • 微调LLAMA-2,一个最先进的LLM,以根据22个独特的数据元素生成综合报告.
    • 使用BERT F1评分和手动验证评估了LLM生成的报告准确性.

    主要成果:

    • 微调的LLAMA-2在所有数据元素中获得了0.86或更高的BERT F1评分.
    • 在50%以上的数据元素中,BERT F1得分为0.94或更高 (共22项中的11项).
    • 报告准确度在临床报告中从76%到81%不等.

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    结论:

    • 通过LLM微调,证明了成功自动生成综合报告.
    • 临床临床医学仪表显示出提高临床文档效率和准确性的巨大潜力.