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使用大型语言模型从外科病理学报告中自动提取数据报告:回顾性队列研究

Denise Lee1, Akhil Vaid2, Kartikeya M Menon1

  • 1Department of Surgery, Icahn School of Medicine at Mount Sinai, 10 Union Square East, Suite 2L, New York, NY, 10003, United States, 1 212 241 2891.

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概括
此摘要是机器生成的。

一个本地部署的大型语言模型 (LLM) 证明了从外科病理报告中回答医疗问题 (MQA) 的显著时间节省,达到89%的准确性. 进一步优化可以增强临床数据提取.

关键词:
在NLP中,我们使用了NLP.人工智能的人工智能是人工智能.内分泌手术是指内分泌手术.一个基本的框架框架.大型语言模型医疗 医疗 医疗 医疗医疗问题 医疗问题自然语言处理自然语言处理.隐私 隐私 隐私 隐私 隐私 隐私报告报告报告报告报告.手术病理学的外科病理学甲状腺癌是一种癌症.

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科学领域:

  • 临床自然语言处理 (NLP)
  • 医疗保健中的人工智能

背景情况:

  • 大型语言模型 (LLM) 为临床NLP任务提供了潜力,例如医疗问题答案 (MQA).
  • 对成本,计算能力和患者隐私的担忧限制了LLM在医疗保健中的采用.
  • 在机构防火墙中部署的开源LLM可以减轻隐私风险.

研究的目的:

  • 从外科病理学报告中评估本地部署的LLM对自动化MQA的性能.
  • 在提取临床信息方面,比较LLM与人类审稿人的准确性和效率.

主要方法:

  • 84份甲状腺癌外科病理报告由两个人体审查员和FastChat-T5 LLM分析.
  • 报告被细分,转换为嵌入式,并为LLM处理进行上下文集成.
  • 提出了12个医学问题,以提取分期和复发风险数据,并评估了响应时间和一致性.

主要成果:

  • 人类审查者实现了99%的一致性.
  • 该LLM与人类审稿人达到了89%的一致性.
  • 与人类审稿人 (170.7分钟和115分钟) 相比,LLM的反应明显更快 (19.56分钟).

结论:

  • 在当地部署的LLM为MQA在临床环境中以可接受的准确性提供了相当大的时间节省.
  • 快速工程和微调可以进一步改进从临床叙述中自动提取数据.
  • 在保证隐私的情况下,LLM对实时临床洞察有希望.