Reference : Machine learning in the social and health sciences

	Document type :	E-prints/Working papers : Already available on another site
	Discipline(s) :	Social & behavioral sciences, psychology : Sociology & social sciences Engineering, computing & technology : Computer science Human health sciences : Public health, health care sciences & services
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/47733

Title :	Machine learning in the social and health sciences
Language :	English
Author, co-author :	Leist, Anja [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Klee, Matthias [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Kim, Jung Hyun [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) >]
	Rehkopf, David []
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
	Muniz-Terrera, Graciela []
	Wade, Sara []
Publication date :	2021
Peer reviewed :	No
Abstract :	[en] The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
Research centres :	Integrative Research Unit: Social and Individual Development (INSIDE) > PEARL Institute for Research on Socio-Economic Inequality (IRSEI)
Funders :	European Commission - EC
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/47733
source URL :	https://arxiv.org/abs/2106.10716
Framework Programme / European Project :	H2020 ; 803239 - CRISP - Cognitive Aging: From Educational Opportunities to Individual Risk Profiles

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