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.
Irish Clinical Academic Training (ICAT) Program, supported by the Welcome Trust and the Health Research Board (Grant Number 203930/B/16/Z), the Health Service Executive, NationalDoctors Training and Planning and the Health and Social Care, Research and
ID Code:
27561
Deposited On:
15 Aug 2022 14:56 by
Thomas Murtagh
. Last Modified 14 Mar 2023 15:57