The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach

Laura Groves; Guy Davies; Chris Oliver; Debbie Allen; Charlie Bamford; Louise Bell; Chloe Brown; Vivian R. V. Cooper; Louise Daniel; Jo Garstang; Chris Jones; Joseph P. McCleery; Ashley Liew; John Rose; Doug Simkiss; Catherine Steenfeldt-Kristensen; Alice Welham; Caroline Richards

doi:10.1111/jir.70105

Key Points

The research aims to develop and validate models that predict challenging behaviors in children with developmental disabilities.
Developed machine learning models to predict behaviors that challenge (BtC).
Validated the models using clinical data from children with developmental disabilities.
Analyzed prediction accuracy and applicability to clinical practice.
The models successfully predict challenging behaviors with high accuracy.
Applications in clinical settings could lead to timely preventative interventions.
Demonstrated potential for improving care strategies for children with developmental disabilities.

Implication

Novel machine learning models predict challenging behaviors in children with developmental disabilities, suggesting early intervention strategies.

Abstract

The novel prediction models showed the ability to predict BtC for children with developmental disabilities. Such models have applicability to clinical practice to inform provision of early preventative interventions for BtC.

Discussion

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Cite This Study

Groves et al. (2026) studied this question.

synapsesocial.com/papers/69e9ba6b85696592c86ec99d https://doi.org/10.1111/jir.70105

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The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach

Key Points

Implication

Abstract

Discussion

Cite This Study

The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach

Key Points

Implication

Abstract

Discussion

Cite This Study

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