[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120279-en":3,"doc-seo-120279-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120279,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning for prediction of childhood mental health problems in social care","Childhood mental health problems are rising in the UK, yet early identification in social care settings remains challenging due to difficulty distinguishing early symptoms, disrupted identification pathways, and varied prevalence estimates. This study characterises a cohort of 26,820 children in Wales receiving social care and develops machine learning models by linking health, social care and education data. Model performance, interpretability, and fairness are evaluated to support earlier intervention and highlight potential algorithmic biases.","|  | BJPsych Open (2025)\u003Cbr>11, e86, 1–9 . doi: 10 . 1192/bjo.2025.32 |\n| --- | --- |\n\n| \u003Cbr>Machine learning for prediction of childhood mental health problems in social care\u003Cbr>Ryan Crowley, Katherine Parkin, Emma Rocheteau, Efthalia Massou, Yasmin Friedmann, Ann John, Rachel Sippy, Pietro Liò and Anna Moore |  |\n| --- | --- |\n| Background\u003Cbr>Rates of childhood mental health problems are increasing in the UK. Early identification of childhood mental health problems is challenging but critical to children’s future psychosocial development. This is particularly important for children with social care contact because earlier identification can facilitate earlier intervention. Clinical prediction tools could improve these early intervention efforts.\u003Cbr>Aims\u003Cbr>Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems.\u003Cbr>Method\u003Cbr>We used linked, de-identified data from the Secure Anonymised Information Linkage 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 machine learning models aimed at predicting childhood mental health problems. We assessed the performance, interpretability and fairness of these models.\u003Cbr>Results\u003Cbr>Risk factors strongly associated with childhood mental health problems included age, substance misuse and being a looked | after child. The best-performing model, a gradient boosting classifier, achieved an area under the receiver operating characteristic curve of 0 .75 (95% CI 0 .73–0.78) . Assessments of algorithmic fairness showed potential biases within these models.\u003Cbr>Conclusions\u003Cbr>Machine learning performance on this prediction task was promising. Predictive performance in social care settings can be bolstered by linking diverse routinely collected data-sets, making available a range of heterogenous risk factors relating to clinical, social and environmental exposures.\u003Cbr>Keywords\u003Cbr>Mental health services; medical technology; community mental health teams; machine learning methods; precision medicine.\u003Cbr>Copyright and usage\u003Cbr>© The Author(s), 2025 . Published by Cambridge University Press on behalf of Royal College of Psychiatrists. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |\n\nChildhood mental health problems  \nThe burden of childhood mental health problems is increasing in the UK, with a recent report placing the prevalence at approximately 16% .1 This increase may stem from a confluence of factors including the COVID-19 pandemic, widening income inequality, social media usage and increased pressure within school settings.2 Children in social care settings have a greater risk of poor mental health outcomes, which may be explained by more frequent exposure to adverse childhood experiences (ACEs) and barriers to accessing care.3 Identifying childhood mental health problems is difficult, particularly for non-specialists, because early symptoms of a disorder can be challenging to disentangle from normal development, children experience different symptoms as they age and they may struggle to explain their feelings and behaviours.4 Mental health problem identification for children with social care contact can be particularly difficult because ACEs can negatively impact development, and the care systems normally responsible for identifying problems in children (e.g. carers, general practitioners and schools) are inconsistent and disrupted. Estimates on the rates of mental health problems in children in social care settings vary, with some figures ranging from 19 to 38% .5,6 Despite t","cbCaijsLayhlpPiQ","https://ap.wps.com/l/cbCaijsLayhlpPiQ","pdf",896207,1,9,"English","en",105,"# Background\n# Aims\n# Method\n# Results\n# Conclusions\n# Clinical prediction tools in psychiatry","[{\"question\":\"Why is early identification of childhood mental health problems difficult in social care settings?\",\"answer\":\"Early symptoms can be hard to separate from normal development, vary with age, and children may struggle to describe feelings and behaviours. For children with social care contact, identification systems are inconsistent and disrupted, making assessment and care access harder.\"},{\"question\":\"How was the study cohort created and what data sources were used?\",\"answer\":\"Linked, de-identified data from the Secure Anonymised Information Linkage Databank were used to build a cohort of 26,820 children in Wales receiving social care. Health, social care, and education data were integrated to develop prediction models.\"},{\"question\":\"Which risk factors and model performance were reported?\",\"answer\":\"Age, substance misuse, and being a looked-after child were strongly associated with childhood mental health problems. The best-performing gradient boosting classifier achieved an AUC of 0.75 (95% CI 0.73–0.78).\"},{\"question\":\"What fairness considerations were found?\",\"answer\":\"Algorithmic fairness assessments indicated potential biases within the predictive models. The study evaluates interpretability and fairness alongside performance to inform responsible use in social care settings.\"}]","Machine learning for prediction of childhood mental health problems in social care | PDF",1785729215,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-for-prediction-of-childhood-mental-health-problems-in-social-care","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-prediction-of-childhood-mental-health-problems-in-social-care/120279/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early identification of childhood mental health problems difficult in social care settings?","Question",{"text":75,"@type":76},"Early symptoms can be hard to separate from normal development, vary with age, and children may struggle to describe feelings and behaviours. For children with social care contact, identification systems are inconsistent and disrupted, making assessment and care access harder.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study cohort created and what data sources were used?",{"text":80,"@type":76},"Linked, de-identified data from the Secure Anonymised Information Linkage Databank were used to build a cohort of 26,820 children in Wales receiving social care. Health, social care, and education data were integrated to develop prediction models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which risk factors and model performance were reported?",{"text":84,"@type":76},"Age, substance misuse, and being a looked-after child were strongly associated with childhood mental health problems. The best-performing gradient boosting classifier achieved an AUC of 0.75 (95% CI 0.73–0.78).",{"name":86,"@type":73,"acceptedAnswer":87},"What fairness considerations were found?",{"text":88,"@type":76},"Algorithmic fairness assessments indicated potential biases within the predictive models. 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