Machine learning for prediction of childhood mental health problems in social care
Background 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 id...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-05-01
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| Series: | BJPsych Open |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2056472425000328/type/journal_article |
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| _version_ | 1850185668045570048 |
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| author | Ryan Crowley Katherine Parkin Emma Rocheteau Efthalia Massou Yasmin Friedmann Ann John Rachel Sippy Pietro Liò Anna Moore |
| author_facet | Ryan Crowley Katherine Parkin Emma Rocheteau Efthalia Massou Yasmin Friedmann Ann John Rachel Sippy Pietro Liò Anna Moore |
| author_sort | Ryan Crowley |
| collection | DOAJ |
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Background
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.
Aims
Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems.
Method
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.
Results
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.
Conclusions
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.
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| format | Article |
| id | doaj-art-27dcd198d9a64fdfb5bc4082ff9ece7a |
| institution | OA Journals |
| issn | 2056-4724 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | BJPsych Open |
| spelling | doaj-art-27dcd198d9a64fdfb5bc4082ff9ece7a2025-08-20T02:16:39ZengCambridge University PressBJPsych Open2056-47242025-05-011110.1192/bjo.2025.32Machine learning for prediction of childhood mental health problems in social careRyan Crowley0https://orcid.org/0000-0002-1482-5631Katherine Parkin1https://orcid.org/0000-0001-7338-5667Emma Rocheteau2https://orcid.org/0000-0002-6450-0878Efthalia Massou3Yasmin Friedmann4Ann John5https://orcid.org/0000-0002-5657-6995Rachel Sippy6Pietro Liò7Anna Moore8https://orcid.org/0000-0001-9614-3812New York University Grossman School of Medicine, New York, USDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK Department of Psychiatry, University of Cambridge, Cambridge, UK Cambridge Public Health, University of Cambridge, Cambridge, UKDepartment of Computer Science, University of Cambridge, Cambridge, UKDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UKNeath Port Talbot County Borough Council, Port Talbot, UKPopulation Psychiatry, Suicide and Informatics, Swansea University Medical School, Swansea, UKDepartment of Psychiatry, University of Cambridge, Cambridge, UKDepartment of Computer Science, University of Cambridge, Cambridge, UKDepartment of Psychiatry, University of Cambridge, Cambridge, UK Anna Freud, London, UK Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK Background 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. Aims Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems. Method 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. Results 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. Conclusions 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. https://www.cambridge.org/core/product/identifier/S2056472425000328/type/journal_articleMental health servicesmedical technologycommunity mental health teamsmachine learning methodsprecision medicine |
| spellingShingle | Ryan Crowley Katherine Parkin Emma Rocheteau Efthalia Massou Yasmin Friedmann Ann John Rachel Sippy Pietro Liò Anna Moore Machine learning for prediction of childhood mental health problems in social care BJPsych Open Mental health services medical technology community mental health teams machine learning methods precision medicine |
| title | Machine learning for prediction of childhood mental health problems in social care |
| title_full | Machine learning for prediction of childhood mental health problems in social care |
| title_fullStr | Machine learning for prediction of childhood mental health problems in social care |
| title_full_unstemmed | Machine learning for prediction of childhood mental health problems in social care |
| title_short | Machine learning for prediction of childhood mental health problems in social care |
| title_sort | machine learning for prediction of childhood mental health problems in social care |
| topic | Mental health services medical technology community mental health teams machine learning methods precision medicine |
| url | https://www.cambridge.org/core/product/identifier/S2056472425000328/type/journal_article |
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