Predicting depression in old age: Combining life course data with machine learning

Reference : Predicting depression in old age: Combining life course data with machine learning


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Business & economic sciences : Special economic topics (health, labor, transportation…)
	To cite this reference:	http://hdl.handle.net/10993/54824

Title :	Predicting depression in old age: Combining life course data with machine learning
Language :	English
Author, co-author :	Montorsi, Carlotta [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > >]
	Fusco, Alessio [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > >]
	van Kerm, Philippe [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	3-Jun-2022
Peer reviewed :	No
Audience :	International
Event name :	Well-Being conference
Event date :	from 01.06.2022 to 4.06.2022
Event organizer :	STATEC
Event place (city) :	Luxembourg
Keywords :	[en] Depression ; Machine Learning ; Aging
Research centres :	Luxembourg Institute of Socio-Economic Research - LISER
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	Predictive well-being in old age
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/54824
FnR project :	FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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