Parkin, Katherine;
Crowley, Ryan;
Sippy, Rachel;
Hayat, Shabina;
Zhang, Yi;
Brewis, Emily;
Marshall, Nicole;
... Moore, Anna; + view all
(2025)
Development of a risk factor framework to inform machine learning prediction of young people's mental health problems: a Delphi study.
JAMIA Open
, 8
(6)
, Article ooaf166. 10.1093/jamiaopen/ooaf166.
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Abstract
OBJECTIVE: To create a theoretical framework of mental health risk factors to inform the development of prediction models for young people’s mental health problems. MATERIALS AND METHODS: We created an initial prototype theoretical framework using a rapid literature search and stakeholder discussion. A snowball sampling approach identified experts for the Delphi study. Round 1 sought consensus on the overall approach, framework domains, and life course stages. Round 2 aimed to establish the points in the life course where exposure to specific risk factors would be most influential. Round 3 ranked risk factors within domains by their predictive importance for young people’s mental health problems. RESULTS: The final framework reached consensus after 3 rounds and included 287 risk factors across 8 domains and 5 life course stages. Twenty-five experts completed round 3. Domains ranked as most important were “Social and Environmental” and “Psychological and Mental Health.” Ranked lists of risk factors within domains and heat maps showing the salience of risk factors across life course stages were generated. DISCUSSION: The study integrated multidisciplinary expert perspectives and prioritized health equity throughout the framework’s development. The ranked risk factor lists and life stage heat maps support the targeted inclusion of risk factors across developmental stages in prediction models. CONCLUSION: This theoretical framework provides a roadmap of important risk factors for inclusion in early identification models to enhance the predictive accuracy of childhood mental health problems. It offers a useful theoretical reference point to support model building for those without domain expertise.
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