As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential to improve the everyday life of the elderly and chronically ill people. This includes, for example, medication reminders, routine checks, or social chit-chat. In addition, conversational agents can satisfy the fundamental need of having access to information about daily news or local events, which enables individuals to stay informed and connected with the world around them. However, finding relevant news sources and navigating the plethora of news articles available online can be overwhelming, particularly for those who may have limited technological literacy or health-related impairments. To address this challenge, we propose an innovative solution that combines knowledge graphs and conversational agents for news search in assisted living. By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people to easily discover relevant news articles and give personalized recommendations. We explain our design choices, provide a system architecture, share insights of an initial user test, and give an outlook on planned future work.
翻译:随着医疗保健部门面临着人口老龄化、人员短缺和常见慢性病等重大挑战,为个人提供高质量的护理变得非常困难。对话代理人已经被证明是缓解这些问题的一种有前途的技术。作为数字健康助手,它们有潜力改善老年人和慢性病人的日常生活。这包括药物提醒、常规检查或社交聊天。此外,对话代理人可以满足获得有关日常新闻或本地事件信息的基本需求,这使个人能够了解并与周围的世界保持联系。然而,对于可能具有有限技术素养或与健康相关的障碍的人来说,查找相关新闻来源并导航在线可用的大量新闻文章可能非常令人不知所措。为了解决这一挑战,我们提出了一种创新的解决方案,将知识图谱和对话代理人结合起来实现辅助生活中的新闻搜索。通过利用图数据库来语义化结构化新闻数据并实现直观的语音界面,我们的系统可以帮助护理依赖的人轻松发现相关新闻文章并提供个性化建议。我们解释了我们的设计选择,提供了一个系统架构,分享了一个初步用户测试的见解,并展望了计划中的未来工作。