Rechercher

[GHM19b] Estimating the Believability of Uncertain Data Inputs in Applications for Alzheimer's Disease Patients

Conférence Internationale avec comité de lecture : 24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019), June 2019, pp.12 , Series LNCS, Salford, United Kingdom,
motcle:
Résumé: Data believability estimation is a crucial issue in many application domains. This is particularly true when handling uncertain input data given by Alzheimer’s disease patients. In this paper, we propose an approach, called DBE_ALZ, to estimate quantitatively the believability of uncertain input data in the context of applications for Alzheimer’s disease patients. In this context, data may be given by Alzheimer’s disease patients or their caregivers. The believa- bility of an input data is estimated based on its reasonableness compared to common-sense standard and personalized rules and the reliability of its authors. This estimation is based on Bayesian networks and Mamdani fuzzy inference systems. We illustrate the usefulness of our approach in the context of the Cap- tain Memo memory prosthesis. Finally, we discuss the encouraging results de- rived from the evaluation of our approach.

BibTeX

@inproceedings {
GHM19b,
title="{Estimating the Believability of Uncertain Data Inputs in Applications for Alzheimer's Disease Patients}",
author=" F. Ghorbel and F. Hamdi and E. Metais ",
booktitle="{24th International Conference on Applications of Natural Language to Information Systems (NLDB 2019)}",
year=2019,
edition="SPRINGER",
month="June",
series="LNCS",
pages="12 ",
address="Salford, United Kingdom",
}