对话式健康代理:一个个性化的大型语言模型驱动的代理框架.
Mahyar Abbasian1, Iman Azimi1, Amir M Rahmani1,2
1Department of Computer Science, University of California Irvine, Irvine, CA 92697-2625, United States.
JAMIA open
|July 7, 2025
概括
开放CHA框架通过先进的解决问题和多式联络分析来增强对话式健康代理 (CHA). 这种开源解决方案与现有模型相比,在糖尿病管理和心理健康评估等领域显著提高了准确性.
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.
- 机器学习用于医疗应用.
背景情况:
- 目前的对话健康代理 (CHA),特别是那些使用大型语言模型 (LLM) 的代理,在多步骤问题解决,个性化和多式联络数据分析方面受到限制.
- 需要一个更强大和可定制的框架来开发先进的CHAs.
研究的目的:
- 引入openCHA,一个开源的LLM驱动的框架,旨在克服现有CHA的局限性.
- 能够开发具有增强能力的CHA,包括知识获取,问题解决,多语言和多模式交互.
主要方法:
- 开发了openCHA,这是一个开源框架,为构建可定制的CHA提供了基础架构和代码库.
- 将LLM与外部数据源集成,以实现可解释性,个性化和可靠性等功能.
- 获取知识,解决问题的能力,以及多语言/多模式对话的能力.
主要成果:
- 通过2个演示和5个使用案例,在多个健康领域证明了openCHA的有效性.
- 在糖尿病患者管理中达到92.1%的准确性,显著超过GPT-4 (51.8%).
- 在食品建议中表现优于GPT-4,在心理健康聊天机器人评估中表现出色,平均绝对误差最低 (0.31).
结论:
- 开放CHA框架使各种医疗保健任务能够开发多种类型的CHA.
- openCHA增强了可解释性,个性化和可靠性的CHA,解决了当前系统的关键局限性.
- 未来的工作重点是改进规划的稳定性,准确性和处理用户查询模两可.
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