Background: Rates of childhood mental health problems are increasing in the United Kingdom. Early identification of childhood mental health problems is challenging but critical to future psycho-social development of children, particularly those with social care contact. Clinical prediction tools could improve these early identification efforts. Aims: Characterise a novel cohort of children in social care and develop and validate effective Machine Learning (ML) models for prediction of childhood mental health problems. Method: We used linked, de-identified data from the Secure Anonymised Information Linkage (SAIL) Databank to create a cohort of 26,820 children in Wales, UK, receiving social care services. Integrating health, social care, and education data, we developed several ML models. We assessed the performance, interpretability, and fairness of these models. Results: Risk factors strongly associated with childhood mental health problems included substance misuse, adoption disruption, and autism. The best-performing model, a Support Vector Machine (SVM) model, achieved an area under the receiver operating characteristic curve (AUROC) of 0.743, with 95% confidence intervals (CI) of 0.724-0.762. Assessments of algorithmic fairness showed potential biases within these models. Conclusion: ML performance on this prediction task was promising but requires refinement before clinical implementation. Given its size and diverse data, the SAIL Databank is an important childhood mental health database for future work.

The authors have declared no competing interest.

All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) and NIHR Applied Research Collaboration East of England. A.M. is funded through an NIHR Clinical Lectureship by Anna Freud National Centre for Children and Families (AFC). The Delphi Study was funded by MRC Adolescent Engagement Awards MR/T046430/1. Data access and data linkage were funded by What Works for Children's Social Care (WWCSC) and Cambridgeshire and Peterborough NHS Foundation Trust (CPFT). KP is funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR) (Grant Reference Number PD-SPH-2015) and the NIHR Applied Research Collaboration (ARC) East of England.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Our application to obtain access to SAIL was reviewed and approved by the internal and external Information Governance Review Panel (IGRP). Since all datasets are anonymised and there is statistical disclosure control for outputs (e.g. reported results must include a minimum of five individuals), there is no legal requirement for the obtainment of individual consent.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The raw data used for this study is housed by the SAIL Databank. This databank is not available publicly, but researchers can access the data following approval by the SAIL IGRP. Information regarding this application process can be found at https://saildatabank.com/application-process/.