从时间个人健康数据生成摘要的框架

Jon Harris, Ching-Hua Chen, Mohammed J. Zaki
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引用次数: 6

摘要

尽管个人追踪个人健康数据(如心率、步数和营养摄入数据)变得更容易了,但在收集数据和生成有意义的摘要以帮助用户更好地理解他们的数据对他们意味着什么之间仍然存在巨大差距。随着对数据的理解程度提高,用户将能够根据新发现的信息采取行动,努力实现他们的健康目标。我们的目标是通过挖掘数据以获得有趣的行为发现,从而弥合数据收集和摘要生成之间的差距,这些行为发现可能会提供有关用户倾向的提示。我们的重点是通过一组信息摘要模板或“原型”来提高时间个人健康数据的可解释性。这些原型既包括帮助用户评估其健康目标的基于评估的摘要,也包括解释其隐性行为的基于模式的摘要。除了个人层面的总结,我们使用的原型也是为群体层面的总结而设计的。我们应用我们的方法从真实的用户健康数据中生成摘要(单变量和多变量),并表明我们的系统生成的摘要既有趣又有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework for Generating Summaries from Temporal Personal Health Data
Although it has become easier for individuals to track their personal health data (e.g., heart rate, step count, and nutrient intake data), there is still a wide chasm between the collection of data and the generation of meaningful summaries to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work toward striving closer to their health goals. We aim to bridge the gap between data collection and summary generation by mining the data for interesting behavioral findings that may provide hints about a user’s tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or “protoforms.” These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual-level summaries, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user health data and show that the summaries our system generates are both interesting and useful.
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来源期刊
CiteScore
10.30
自引率
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