000			03725 am a22002413u 4500
042			_adc
100	1	0	_aBowe, Andrea K. _eauthor _93136
700	1	0	_aLightbody, Gordon _eauthor _93137
700	1	0	_aStaines, Anthony _eauthor _93138
700	1	0	_aMurray, Deirdre M. _eauthor _93139
245	0	0	_aBig data, machine learning, and population health: predicting cognitive outcomes in childhood
260			_bNature Publishing Group US, _c2022-06-09.
500			_a/pmc/articles/PMC7614199/
500			_a/pubmed/35681091
520			_aABSTRACT: The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such challenge is addressing disparities in early childhood cognitive development-a complex public health issue rooted in the social determinants of health, exacerbated by inequity, characterised by intergenerational transmission, and which will continue unabated without novel approaches to address it. Early life, the period of optimal neuroplasticity, presents a window of opportunity for early intervention to improve cognitive development. Unfortunately for many, this window will be missed, and intervention may never occur or occur only when overt signs of cognitive delay manifest. In this review, we explore the potential value of ML and big data analysis in the early identification of children at risk for poor cognitive outcome, an area where there is an apparent dearth of research. We compare and contrast traditional statistical methods with ML approaches, provide examples of how ML has been used to date in the field of neurodevelopmental disorders, and present a discussion of the opportunities and risks associated with its use at a population level. The review concludes by highlighting potential directions for future research in this area. IMPACT: To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review provides an overview of the public health challenge we face in addressing disparities in childhood cognitive development and focuses on the cornerstone of early intervention. Recent advances in our ability to collect large volumes of data, and in analytic capabilities, provide a potential opportunity to improve current practices in this field. This review explores the potential role of machine learning and big data analysis in the early identification of children at risk for poor cognitive outcomes.
540			_a© The Author(s) 2022
540			_ahttps://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
546			_aen
690			_aReview Article _93140
655	7		_aText _2local
786	0		_nPediatr Res
856	4	1	_uhttp://dx.doi.org/10.1038/s41390-022-02137-1 _zConnect to this object online.
999			_c2233 _d2233