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逐渐受到关注，用于分析个体轨迹数据（如电子健康记录）。然而，当数据遵循复杂的时间模式时，这一任务变得显著困难。本文提出时序表型的概念，将其定义为特征随时间变化的排列方式，并创新性地提出SWoTTeD（时序张量分解滑动窗口）方法，用于发现隐藏的时间模式。SWoTTeD集成了多种约束与正则化技术以增强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了该方法的有效性，并基于巴黎公立医院集团的数据提出了原创性应用案例。结果表明，SWoTTeD在重构精度上至少达到当前最先进的张量分解模型水平，且能提取对临床医生具有实际意义的时序表型。