SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping

• URL: http://arxiv.org/abs/2310.01201v3
• Date: Thu, 28 Mar 2024 15:09:13 GMT
• Title: SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping
• Authors: Hana Sebia, Thomas Guyet, Etienne Audureau,
• Abstract summary: We propose SWoTTeD (Sliding Window for Temporal Decomposition), a novel method to discover hidden temporal patterns. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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