Abstract
Prediction models are widely used in insurance companies and health services. Even when 120 million people are at risk of suffering poverty or social exclusion in the EU, this kind of models are surprisingly unusual in the field of social services. A fundamental reason for this gap is the difficulty in labeling and annotating social services data. Conditions such as social exclusion require a case-by-case debate. This paper presents a multi-agent architecture that combines semantic web technologies, exploratory data analysis techniques, and supervised machine learning methods. The architecture offers a holistic view of the main challenges involved in labeling data and generating prediction models for social services. Moreover, the proposal discusses to what extent these tasks may be automated by intelligent agents.
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Notes
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In this vein, the adult dataset [8] is a well known public labeled dataset that allows predicting whether an adult income exceeds $50K a year based on a 1994 census database. It can be used to train prediction models as a proof of concept before collecting and labeling the own proprietary data.
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Acknowledgments
This research work is supported by the Spanish Ministry of Economy, Industry and Competitiveness under the R&D project Datos 4.0: Retos y soluciones (TIN2016-78011-C4-4-R, AEI/FEDER, UE).
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Serrano, E., Bajo, J. (2017). Towards Social Care Prediction Services Aided by Multi-agent Systems. In: Montagna, S., Abreu, P., Giroux, S., Schumacher, M. (eds) Agents and Multi-Agent Systems for Health Care. A2HC AHEALTH 2017 2017. Lecture Notes in Computer Science(), vol 10685. Springer, Cham. https://doi.org/10.1007/978-3-319-70887-4_7
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