挑战昼夜节律：在UK生物库数据中聚类参与者遥测
Defying the Circadian Rhythm: Clustering Participant Telemetry in the UK Biobank Data
摘要：
英国生物银行数据集跟踪了超过50万名志愿者，包含了与社会结果相关的各种信息。在这个庞大的集合中，从腕戴式加速度计收集的大量遥测数据提供了参与者活动的快照。使用这些数据，人口的轮班工人，受到破坏的昼夜节律，使用混合模型为基础的方法进行分析，以产生保护作用，从体力活动的生存结果。在本文中，我们开发了一种可扩展的，标准化的，独特的方法，有效地集群大量的参与者遥测。通过建立在Doherty et al.（2017）的工作基础上，我们引入了一个标准化的低维特征用于聚类目的。使用基于矩阵变量混合模型的方法对参与者进行聚类。一旦聚类，进行生存分析以证明每个聚类内个体的不同终生结果。总之，我们处理，聚类和分析英国生物银行参与者的一个子集，以显示身体活动对昼夜节律紊乱的个体的保护作用。
英文摘要：
The UK Biobank dataset follows over 500,000 volunteers and contains a diverse set of information related to societal outcomes. Among this vast collection, a large quantity of telemetry collected from wrist-worn accelerometers provides a snapshot of participant activity. Using this data, a population of shift workers, subjected to disrupted circadian rhythms, is analysed using a mixture model-based approach to yield protective effects from physical activity on survival outcomes. In this paper, we develop a scalable, standardized, and unique methodology that efficiently clusters a vast quantity of participant telemetry. By building upon the work of Doherty et al. (2017), we introduce a standardized, low-dimensional feature for clustering purposes. Participants are clustered using a matrix variate mixture model-based approach. Once clustered, survival analysis is performed to demonstrate distinct lifetime outcomes for individuals within each cluster. In summary, we process, cluster, and analyse a subset of UK Biobank participants to show the protective effects from physical activity on circadian disrupted individuals.
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。
