动态社交网络预测个人心理健康的力量
The power of dynamic social networks to predict individuals' mental health
摘要：
精准医疗在临床内外都受到了关注。我们专注于后者，通过利用个人的社会互动与心理健康之间的关系，从丰富的动态社交网络数据中开发一个人抑郁或焦虑的可能性的预测模型。据我们所知，我们是第一个这样做的。现有的研究与我们的工作至少在一个方面不同：他们没有将社交互动数据建模为网络;他们这样做，但分析静态网络数据;他们检查社交网络和健康之间的“相关性”，但没有开发预测模型;或者他们研究其他个人特征，但不研究心理健康。在一个系统和全面的评估中，我们表明，我们的预测模型，使用动态的社交网络数据是优于其静态网络以及非网络等价物时，运行在相同的数据。
英文摘要：
Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our knowledge, we are the first to do this. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without developing a predictive model; or they study other individual traits but not mental health. In a systematic and comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
