综合性定性分析患者对话总结使用大型语言模型应用于噪音,非正式,非英语真实世界的数据
Anderson A Ferreira1,2, Leonardo Rocha3, Washington Cunha4
1Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. anderson.ferreira@ufop.edu.br.
大型语言模型 (LLM) 有效地用葡萄牙语总结了杂,非正式的患者与医疗对话. LLaMA3的表现略高于Qwen2,显示了提高数字健康沟通效率和患者满意度的潜力.
科学领域:
- 自然语言处理
- 数字健康传播
- 医疗保健中的人工智能
背景情况:
- 医疗团队需要有效访问患者信息以进行个性化沟通.
- 数字健康平台通常涉及非正式的真实世界通信数据.
- 总结患者与提供者之间的对话可以提高响应时间和准确性.
研究的目的:
- 评估用于总结患者与医疗团队对话的大型语言模型 (LLM).
- 评估在杂,非正式和代表性不足的语言数据 (葡萄牙语) 上的LLM表现.
- 确定在现实世界电子健康环境中使用LLM生成的摘要的可行性.
主要方法:
- 收集和匿名葡萄牙语WhatsApp消息的数据集.
- 评估对话质量 (大小,可读性,正确性).
- 使用LLaMA3和Qwen2生成的摘要与特定提示,由志愿者对覆盖范围,相关性,冗余性和真实性进行评估.
主要成果:
- 即使使用低质量,代表性不足的语言数据,LLM也可以产生有效的患者医疗对话摘要.
- 在总结覆盖率和真实性方面,LLaMA3比Qwen2略有优势.
- 尽管数据具有挑战性,但志愿者评估表明总结成功.
结论:
- 在帮助医疗保健专业人员处理患者信息响应方面,LLM具有显著的应用潜力.
- 这种方法可以提高数字医疗保健的沟通效率,清晰度和患者满意度.
- 这些发现对资源有限,初级保健服务有限的环境尤为重要.
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