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聊天-ePRO:基于ChatGPT的电子患者报告结果系统的开发和试点研究

Zikang Chen¹, Qinchuan Wang², Yaoqian Sun¹

¹College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Journal of biomedical informatics

|May 4, 2024

概括

大型语言模型 (LLM) 增强了电子患者报告结果 (ePRO) 系统. 使用ChatGPT构建的新Chat-ePRO系统在试点研究中显示了患者合规性和数据收集效率的提高.

科学领域:

数字健康数字健康
医疗保健中的人工智能
患者报告的结果

背景情况:

电子患者报告结果 (ePRO) 系统对于数据收集至关重要.
大型语言模型 (LLM) 对ePRO系统的传统基于规则的聊天机器人提供了优势,包括简化开发和增强的对话灵活性.
在ePRO系统中LLMs的实际应用目前有限.

研究的目的:

探索建立基于LLMs的ePRO系统的可行性.
开发和评估一个新的基于LLM的ePRO系统,名为Chat-ePRO.
评估LLM在提高用户在ePRO数据收集中的合规性方面的潜力.

主要方法:

在微信小程序平台上使用ChatGPT开发了Chat-ePRO系统.
雇佣了对话算法的快速工程和离线知识蒸.
进行了一项试点研究,将Chat-ePRO与基于表单和基于规则的聊天机器人ePRO系统进行比较.

主要成果:

聊天-ePRO在从PRO表单中生成对话方面取得了95.7%的成功率,在数据提取方面获得了0.95的宏F1得分.
超过70%的医生发现Chat-ePRO理解问题并产生一致的响应.
试点研究表明,与其他方法相比,Chat-ePRO的响应率更高 (90%) 和交互时间更长.

结论:

使用提示工程来驱动LLM是可行的ePRO数据收集.
聊天-ePRO系统有效地提高了患者对ePRO数据收集的遵守.
基于LLM的聊天机器人代表了患者数据管理的数字健康方面有前途的进步.

关键词:

乳腺癌乳腺癌乳腺癌知识蒸知识蒸大型语言模型移动健康服务提供者患者报告的结果措施

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