SWoTTeD : An Extension of Tensor Decomposition to Temporal Phenotyping
In: https://hal.science/hal-04310487 ; 2023, 2023
videoRecording
Zugriff:
Tensor decomposition has recently been gaining attention in the machine learning community due to its versatility in processing large-scale data. In particular, it has become popular for the analysis of Electronic Health Records (EHR). However, this task becomes signifi- cantly more difficult when the data follows complex temporal patterns. This paper introduces the notion of a temporal phenotype as an arrange- ment 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 regu- larizations to enhance the interpretability of the extracted phenotypes. We validate our proposal using both synthetic and real-world patient data from MIMIC-IV and the Greater Paris University Hospital. The results show that SWoTTeD outperforms the recent state-of-the-art tensor decomposition models.
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SWoTTeD : An Extension of Tensor Decomposition to Temporal Phenotyping
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Autor/in / Beteiligte Person: | Sebia, Hana ; La pharmacologie des neurones et des astrocytes à l’aide des sciences du numérique (AISTROSIGHT) ; Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Centre Hospitalier Lyon Sud CHU - HCL (CHLS) ; Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL)-Theranexus Lyon -Inria Lyon ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
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Zeitschrift: | https://hal.science/hal-04310487 ; 2023, 2023 |
Veröffentlichung: | HAL CCSD, 2023 |
Medientyp: | videoRecording |
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