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Temporal Data Simulation based on a real data set for fall prevention

Abstract : The motivation of this article is the need for a large temporal data set, which allows to reason about the changes regarding person’s features over a long period of time. Faced with the difficulty of finding such a data set, we propose an algorithm to simulate such a data set, based on real static data provided by the service of fall prevention of Lille’s hospital. We select five persistent variables, meaning that their value may change at most once, toward positive value for positive persistent variables. The algorithm is based on assumptions regarding the temporal evolution of each contextualized variable, as defined by a Bayesian network learned on the real static data set. The temporal data set simulated thanks to the proposed algorithm is evaluated by the comparison of the temporal distribution of each contextualized variable with the functions obtained by linear interpolation from the real data set.
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Conference papers
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Submitted on : Thursday, October 21, 2021 - 10:05:39 AM
Last modification on : Monday, May 16, 2022 - 6:50:01 PM


  • HAL Id : hal-03389621, version 1



Gulshan Sihag, Véronique Delcroix, Emmanuelle Grislin-Le Strugeon, Xavier Siebert, Sylvain Piechowiak. Temporal Data Simulation based on a real data set for fall prevention. 10èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilis, Oct 2021, Île De Porquerolles, France. ⟨hal-03389621⟩



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