实施焦虑英国的问问焦虑聊天机器人服务:教训
Luke Collins1, Niamh Nicholson2, Nicky Lidbetter2
1Linguistics and English Language, Lancaster University, Lancaster, United Kingdom.
JMIR human factors
|June 17, 2024
概括
像Ask Anxia这样的健康聊天机器人,通过管理重复的任务和提供关键的健康信息,提供了可访问的支持. 持续监控和语料库语言学优化聊天机器人的性能和用户查询响应.
科学领域:
- 数字健康数字健康
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 医疗保健系统面临人力资源的限制,增加了对可访问数字服务的需求.
- 简单的,基于规则的聊天机器人对于大量的,重复性的健康信息传递是有效的.
- 焦虑英国实施了Ask Anxia聊天机器人,以增强现有的支持服务.
研究的目的:
- 报告Ask Anxia聊天机器人的实施和代开发情况.
- 展示语料库语言学在评估用户查询和优化聊天机器人响应方面的实用性.
- 为开发自动化对话接口的组织提供建议.
主要方法:
- 在18个月的时间里,Ask Anxia聊天机器人的代开发和性能监测.
- 使用语料库语言学分析用户查询,以确定反复出现的主题.
- 根据用户查询分析改进聊天机器人信息和服务.
主要成果:
- 在18个月的时间里,Ask Anxia聊天机器人以响应用户需求而显著发展.
- 语料库语言学在评估用户查询和增强聊天机器人响应方面被证明是有效的.
- 开发过程强调了聊天机器人在医疗保健信息提供中的潜力.
结论:
- 像健康聊天机器人一样,自动化对话接口可以有效地补充医疗保健中的人力资源.
- 语料库语言学是优化聊天机器人性能和用户体验的宝贵工具.
- 为希望实施类似聊天机器人服务的组织提供建议.
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