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The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental
Laura Groves1,2, Guy Davies3, Chris Oliver1
1School of Psychology, University of Birmingham, Birmingham, UK.
Insights
Machine learning models accurately predict behaviours that challenge (BtC) in children with developmental disabilities. These models can identify children at high risk, enabling targeted early interventions for better outcomes.
Area of Science:
- Developmental Psychology
- Machine Learning in Healthcare
- Behavioral Science
Background:
- Children with developmental disabilities frequently exhibit behaviours that challenge (BtC).
- Identifying risk markers is crucial for early intervention strategies.
- Predicting BtC can improve support for affected children and families.
Purpose of the Study:
- To develop and validate machine learning models for predicting risk of BtC in children with developmental disabilities.
- To assess the models' ability to predict specific types of BtC, including self-injurious behavior, aggression, and property destruction.
- To evaluate the external validity of these prediction models for future behavioral outcomes.
Main Methods:
- Caregiver-reported data on behaviours that challenge were collected using the Self-injury, Aggression and Destruction Screening Questionnaire (SAD-SQ).
- Machine learning algorithms, including random forest classifiers and Gaussian mixture models (GMM), were trained and tested on a dataset of 778 individuals.
- External validation was performed on a separate dataset of 121 individuals, assessing predictions over a 12-month period.
Main Results:
- Random forest classifiers and GMM algorithms demonstrated high accuracy in predicting 'any BtC', correctly classifying 83.5% of individuals at risk.
- The models showed good performance in predicting the persistence and incidence of BtC over 12 months, with accuracy rates of 83.5% and 83.3%, respectively.
- The study achieved fair to good recall and precision for predicting various BtC categories.
Conclusions:
- Novel machine learning models effectively predict behaviours that challenge in children with developmental disabilities.
- These predictive models hold significant potential for clinical application in guiding early preventative interventions.
- The findings support the use of AI-driven tools to enhance support and reduce the impact of BtC in this population.
Background:
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.
Method:
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.
Outcomes:
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).
Interpretation:
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.
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