基于聊天GPT:软件开发和验证的韩国患者友好的出院总结:软件开发和验证
Hanjae Kim1, Hee Min Jin2, Yoon Bin Jung3
1College of Nursing, Yonsei University, Seoul, Korea.
Journal of Korean medical science
|April 30, 2024
概括
大型语言模型可以生成对患者友好的出院摘要. 几次射击提示比零次射击产生更好的结果,改善了患者的理解和护理.
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.
- 医疗信息学 医疗信息学
背景情况:
- 患者友好的出院总结可以提高理解和满意度.
- 生成这些摘要可以增加医务人员的工作量.
- 大型语言模型 (LLM) 为自动化摘要生成提供了一个潜在的解决方案.
研究的目的:
- 开发和验证使用LLM的软件,以生成对患者友好的出院摘要.
- 为了比较不同提示方法 (零射击,一射击,几射击) 在生成摘要方面的有效性.
- 评估LLM生成的摘要的质量和事实性.
主要方法:
- 开发并测试了LLM软件在100个出院摘要 (心肌梗塞和整体外科手术).
- 使用零拍摄,一拍摄和几拍摄提示生成每个文档的三个摘要.
- 评估的摘要使用一个5点的利克特级别的事实性,全面性,可用性,易用性和流利性.
主要成果:
- 几次射击和一次射击提示明显超过零次射击提示 (平均得分为4.19和4.11对比3.73).
- 77.0% (几次射击) 和70.0% (一次射击) 的摘要得分≥4,而零次射击的得分为32.0%.
- 所有方法的真实性得分都很高,Few-shot和One-shot显示出很好的结果 (≥4.19).
结论:
- LLM,特别是具有Few-shot提示的LLM,可以生成可接受的,对患者友好的出院摘要.
- 这项技术有望提高以患者为中心的护理,特别是对于韩国患者.
- 这项研究证实了人工智能在提高医疗沟通效率和质量方面的潜力.
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