The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach.
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 predi...
| Publicado en: | Journal of Intellectual Disability Research Vol. 70; no. 7; pp. 700 - 709 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jul2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194362161&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194362161 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09642633 EUL jtl: Journal of Intellectual Disability Research issn: 09642633 maglogo: Y pubinfo: dt: Jul2026 vid: 70 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 194362161 193115356 194362161 194362161 10.1111/jir.70105 194362161 ppf: 700 ppct: 9 formats: tig: atl: The Development and Validation of Models of Risk for Behaviours That Challenge in Children With Developmental Disabilities: A Novel Machine Learning Approach. aug: au: Groves, Laura Davies, Guy Oliver, Chris Allen, Debbie Bamford, Charlie Bell, Louise Brown, Chloe Cooper, Vivian Daniel, Louise Garstang, Jo Jones, Chris McCleery, Joseph P. Liew, Ashley Rose, John Simkiss, Doug Steenfeldt‐Kristensen, Catherine Welham, Alice Richards, Caroline affil: School of Psychology, University of Birmingham, Birmingham, UK sug: subj: Developmental Disabilities Children with Disabilities Psychosocial Factors Child Behavior Disorders Risk Factors Self-Injurious Behavior Risk Factors Aggression Risk Factors Disruptive Behavior Evaluation Risk Assessment Machine Learning Prediction Models Evaluation Human Funding Source Validation Studies Parents of Children with Disabilities Caregivers Questionnaires Self-Injurious Behavior Evaluation Aggression Evaluation Impulsive Behavior Evaluation Random Forest Multiple Logistic Regression Descriptive Statistics Infant Child, Preschool Child Adolescence Male Female Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|