In healthcare, patient data is often collected as multivariate time series, providing a comprehensive view of a patient's health status over time. While this data can be sparse, connected devices may enhance its frequency. The goal is to create patient profiles from these time series. In the absence of labels, a predictive model can be used to predict future values while forming a latent cluster space, evaluated based on predictive performance. We compare two models on Withing's datasets, M AGMAC LUST which clusters entire time series and DGM${}^2$ which allows the group affiliation of an individual to change over time (dynamic clustering).

翻译：在医疗保健领域，患者数据通常以多变量时间序列的形式收集，能够全面反映患者随时间变化的健康状况。尽管这些数据可能较为稀疏，但连接设备可提升其采集频率。本研究的目的是从这些时间序列中构建患者画像。在缺乏标签的情况下，可采用预测模型来预测未来值，同时形成潜在聚类空间，并根据预测性能对其进行评估。我们在Withing的数据集上比较了两种模型：M AGMAC LUST（对整个时间序列进行聚类）和DGM${}^2$（允许个体所属群体随时间变化，即动态聚类）。