The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach.
Children with developmental disabilities show a high prevalence of behaviours that challenge (BtC). Thus, harnessing known risk markers to target early intervention to children at the greatest risk of BtC is essential. In this study, machine learning techniques were used to develop prediction models of risk (no, low and high severity behaviour) for different BtC (self-injurious behaviour, aggression, property destruction, 'any BtC'). A secondary aim was to assess the external validation of these models to predict future behaviour.
Caregivers of individuals with developmental disabilities completed the Self-injury, Aggression and Destruction Screening Questionnaire. One dataset (n = 778) was used to train and test models to establish internal validation. Algorithms were created using random forest classifiers, K-nearest neighbours, multiple logistic regressions and Gaussian mixture models (GMM) for each type of behaviour. External validation utilising a second dataset of caregivers (n = 121) completing the SAD-SQ at baseline and 12 months later was then conducted.
Across internal and external validation, the random forest classifiers and GMM algorithms for any BtC showed the highest number of correct classifications with fair to good recall and precision, with 83.5% of people at risk of BtC correctly predicted. Predictions of persistence and incidence of behaviour over 12 months was also good (83.5% and 83.3%, respectively).
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.
Caregivers of individuals with developmental disabilities completed the Self-injury, Aggression and Destruction Screening Questionnaire. One dataset (n = 778) was used to train and test models to establish internal validation. Algorithms were created using random forest classifiers, K-nearest neighbours, multiple logistic regressions and Gaussian mixture models (GMM) for each type of behaviour. External validation utilising a second dataset of caregivers (n = 121) completing the SAD-SQ at baseline and 12 months later was then conducted.
Across internal and external validation, the random forest classifiers and GMM algorithms for any BtC showed the highest number of correct classifications with fair to good recall and precision, with 83.5% of people at risk of BtC correctly predicted. Predictions of persistence and incidence of behaviour over 12 months was also good (83.5% and 83.3%, respectively).
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.
Authors
Groves Groves, Davies Davies, Oliver Oliver, Allen Allen, Bamford Bamford, Bell Bell, Brown Brown, Cooper Cooper, Daniel Daniel, Garstang Garstang, Jones Jones, McCleery McCleery, Liew Liew, Rose Rose, Simkiss Simkiss, Steenfeldt-Kristensen Steenfeldt-Kristensen, Welham Welham, Richards Richards
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