Suppr超能文献

SENHANCE:一种用于在电子健康领域集成社交和硬件传感器的语义网框架。

SENHANCE: A Semantic Web framework for integrating social and hardware sensors in e-Health.

作者信息

Pagkalos Ioannis, Petrou Loukas

机构信息

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece.

出版信息

Health Informatics J. 2016 Sep;22(3):505-22. doi: 10.1177/1460458215571642. Epub 2015 Mar 10.

Abstract

Self-reported data are very important in Healthcare, especially when combined with data from sensors. Social Networking Sites, such as Facebook, are a promising source of not only self-reported data but also social data, which are otherwise difficult to obtain. Due to their unstructured nature, providing information that is meaningful to health professionals from this source is a daunting task. To this end, we employ Social Network Applications as Social Sensors that gather structured data and use Semantic Web technologies to fuse them with hardware sensor data, effectively integrating both sources. We show that this combination of social and hardware sensor observations creates a novel space that can be used for a variety of feature-rich e-Health applications. We present the design of our prototype framework, SENHANCE, and our findings from its pilot application in the NutriHeAl project, where a Facebook app is integrated with Fitbit digital pedometers for Lifestyle monitoring.

摘要

自我报告的数据在医疗保健领域非常重要,尤其是与传感器数据相结合时。社交网站,如脸书,不仅是自我报告数据的一个有前景的来源,也是社交数据的来源,而社交数据 otherwise 很难获得。由于其非结构化的性质,从这个来源提供对医疗专业人员有意义的信息是一项艰巨的任务。为此,我们将社交网络应用程序用作社交传感器,收集结构化数据,并使用语义网技术将其与硬件传感器数据融合,有效地整合这两个来源。我们表明,社交和硬件传感器观测的这种结合创造了一个新颖的空间,可用于各种功能丰富的电子健康应用程序。我们展示了我们的原型框架SENHANCE的设计,以及我们在NutriHeAl项目中的试点应用的结果,在该项目中,一个脸书应用程序与Fitbit数字计步器集成用于生活方式监测。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验