FERTE, Thomas; DUTARTRE, Dan; HEJBLUM, Boris; GRIFFIER, Romain; JOUHET, Vianney; THIEBAUT, Rodolphe; LEGRAND, Pierrick; HINAUT, Xavier

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FERTE, Thomas
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

DUTARTRE, Dan
Service Expérimentation et Développement [Bordeaux] [SED]

HEJBLUM, Boris

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

Langue

Communication dans un congrès

Ce document a été publié dans

International Conference on Machine Learning, 21-27 July 2024, Vienna, Austria, Proceedings of the 41 st International Conference on Machine Learning, 2024-07-21, Vienne. 2024-07-08, vol. 235

Résumé en anglais

In this work, we aimed at forecasting the number of SARS-CoV-2 hospitalized patients at 14 days to help anticipate the bed requirements of a large scale hospital using public data and electronic health records data. Previous attempts ledto mitigated performance in this high-dimension setting; we introduce a novel approach to time series forecasting by providing an alternative to conventional methods to deal with high number of potential features of interest (409 predictors). We integrate Reservoir Computing (RC) with feature selection using a genetic algorithm (GA) to gatheroptimal non-linear combinations of inputs to improve prediction in sample-efficient context. We illustrate that the RC-GA combination exhibitsexcellent performance in forecasting SARS-CoV-2 hospitalizations. This approach outperformed the use of RC alone and other conventional methods: LSTM, Transformers, Elastic-Net, XGBoost. Notably, this work marks the pioneering use of RC (along with GA) in the realm of short and high-dimensional time series, positioning it as a competitive and innovative approach in comparison to standard methods.< Réduire

URI

https://oskar-bordeaux.fr/handle/20.500.12278/201744

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219