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- W3204366752 abstract "The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree." @default.
- W3204366752 created "2021-10-11" @default.
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- W3204366752 date "2021-01-01" @default.
- W3204366752 modified "2023-09-26" @default.
- W3204366752 title "Probabilistic causal model for the detection of obsolete personal information to prevent falls in the elderly" @default.
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- W3204366752 doi "https://doi.org/10.1016/j.procs.2021.08.120" @default.
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