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Pregnancy, birth and early childhood

Predicting developmental delays in children

Goal
To develop and validate machine learning models to accurately identify developmental delays using data from routine screening tests
Impact
Early detection of children with developmental delay will allow early intervention to improve the short- and long-term outcomes.
Our joint work with Israel’s Ministry of Health (MOH) is aimed at improving the national guidelines for the developmental delay screening process

Collaborator(s)
Israel’s Ministry of Health

Recent statistics indicate that as many as 1 in 4 children, between the ages of 0 and 5, are at moderate or high risk for developmental, behavioral, or social delays. Early detection of children with developmental delays, typically done by screening tests, is crucial for determining which children require close surveillance and intervention. In this study, we aim to develop a reliable multicultural screening model that will allow early identification of children with suspected developmental delays.

We use different sources of data such as developmental screening records from Israel’s MOH family health centers (Tipat Halav) and child development records from the UK’s primary care system. One data source will be used to develop an integrative risk score for developmental delays in motor, language, and social skills. The transferability of this model to a different population and a different health system will be evaluated on other data sources.

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