Tensor decomposition has recently been gaining attention in the machine learning community for the analysis of individual traces, such as Electronic Health Records (EHR). However, this task becomes significantly more difficult when the data follows complex temporal patterns. This paper introduces the notion of a temporal phenotype as an arrangement of features over time and it proposes SWoTTeD (Sliding Window for Temporal Tensor Decomposition), a novel method to discover hidden temporal patterns. SWoTTeD integrates several constraints and regularizations to enhance the interpretability of the extracted phenotypes. We validate our proposal using both synthetic and real-world datasets, and we present an original usecase using data from the Greater Paris University Hospital. The results show that SWoTTeD achieves at least as accurate reconstruction as recent state-of-the-art tensor decomposition models, and extracts temporal phenotypes that are meaningful for clinicians.

翻译：张量分解近年来在机器学习领域受到关注，用于分析个体轨迹数据（如电子健康记录）。然而，当数据呈现复杂的时间模式时，该任务变得尤为困难。本文提出时间表型的概念，将其定义为特征随时间排列的模式，并创新性地提出SWoTTeD（滑动窗口时间张量分解方法）以发现隐藏的时间模式。SWoTTeD整合了多种约束与正则化策略，以增强所提取表型的可解释性。我们通过合成数据集与真实世界数据集验证该方法，并展示了一个来自巴黎公立医院集团的原创应用案例。结果表明，SWoTTeD在重构精度上至少达到当前最先进张量分解模型的水平，并能提取对临床医生具有实际意义的时间表型。