Poverty is a multidimensional concept that, besides the economic status and financial resources, should consider the lack of access to resources enabling a minimum standard of living and participation in society. In particular, elderly people are likely to require help with some or everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health. This work proposes a strategy based on Bayesian Network to identify the risk of poverty in elderly people, relying on multidimensional indicators learned from heterogeneous sources of information, including the difficulty of accessing services, social exclusion and health status. Data cleaning and integration that include socio-demographic indicators are proposed here, and an overall framework of analysis that can be exportable to several other categories of the population at risk of poverty is presented.
Bandini, S., Borodi, V., Chieregato, D., Cremaschi, M., D'Antico, F., Messina, V., et al. (2022). Artificial Intelligence facing Multidimensional Poverty in Elderly. In Proceedings of the Italian Workshop on Artificial Intelligence for an Ageing Society 2022 co-located with 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022) (pp.59-72). CEUR-WS.
Artificial Intelligence facing Multidimensional Poverty in Elderly
Bandini S.;Borodi V. M.;Cremaschi M.;Messina V.;Terraneo M.;Terzera L.;Gasparini F.
2022
Abstract
Poverty is a multidimensional concept that, besides the economic status and financial resources, should consider the lack of access to resources enabling a minimum standard of living and participation in society. In particular, elderly people are likely to require help with some or everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health. This work proposes a strategy based on Bayesian Network to identify the risk of poverty in elderly people, relying on multidimensional indicators learned from heterogeneous sources of information, including the difficulty of accessing services, social exclusion and health status. Data cleaning and integration that include socio-demographic indicators are proposed here, and an overall framework of analysis that can be exportable to several other categories of the population at risk of poverty is presented.
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