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利用大型语言模型改善与癌症患者在呼叫中心环境中的沟通理解:概念验证研究

Seungbeom Cho1, Mangyeong Lee2,3, Jaewook Yu1

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像GPT-4这样的大型语言模型可以准确地分类癌症患者在电话中的意图,优于传统模型. 这种人工智能进步可以提高对复杂患者需求的理解,而无需进行广泛的数据培训.

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在法律上,LLMs.在NLP中,我们使用了NLP.癌症 癌症 癌症 癌症 癌症大型语言模型自然语言处理自然语言处理.患者沟通 患者沟通自己管理的自我管理.支持性护理 支持性护理电话咨询服务 电话咨询服务通过电话咨询咨询.分选服务分选服务.

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科学领域:

  • 在瘤学中使用人工智能
  • 医疗保健的自然语言处理.

背景情况:

  • 医院呼叫中心对于癌症患者的支持至关重要,需要准确的意图识别.
  • 传统的深度学习模型 (LSTM,BERT) 需要大量的注释数据,这给效率带来了挑战.
  • 大型语言模型 (LLM) 提供了一个有前途的替代方案,用于在语境学习的意图分类.

研究的目的:

  • 评估GPT-4在癌症相关电话咨询期间对患者意图进行分类的性能.
  • 将GPT-4的准确性与LSTM和BERT进行比较,特别是对于模两可和复杂的查询.

主要方法:

  • 利用了来自癌症患者电话咨询的430,355个句子 (2016-2020) 的数据集.
  • 在300,000个句子上使用监督学习的训练LSTM和BERT模型.
  • 应用了GPT-4使用零射击和少数射击方法而不需要重新培训,比较1000句话的准确性.

主要成果:

  • 在短时间学习中,GPT-4的准确率达到85.2%,远远超过LSTM (73.7%) 和BERT (71.3%).
  • 在"治疗"和"症状"等复杂类别中,GPT-4在处理模两可的查询方面有超过15%的改进.
  • GPT-4在清晰上下文类别 ("记录"",例行") 中表现出色,证明了其在解释患者互动方面的多功能性.

结论:

  • 在瘤咨询中,GPT-4显示了提高患者意图分类的巨大潜力.
  • 它能够在不需要再培训的情况下管理复杂的查询,这比现有的歧视性模型具有关键优势.
  • 建议进一步研究快速设计和混合人工智能系统,以实现实际医疗整合.