Prediction of mental health risk in adolescents
- PMID: 40044931
- PMCID: PMC12176513
- DOI: 10.1038/s41591-025-03560-7
Prediction of mental health risk in adolescents
Erratum in
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Author Correction: Prediction of mental health risk in adolescents.Nat Med. 2025 Jul;31(7):2453. doi: 10.1038/s41591-025-03769-6. Nat Med. 2025. PMID: 40389746 No abstract available.
Abstract
Prospective prediction of mental health risk in adolescence can facilitate early preventive interventions. Here, using psychosocial questionnaires and neuroimaging measures from over 11,000 children in the Adolescent Brain and Cognitive Development Study, we trained neural network models to stratify general psychopathology risk. The model trained on current symptoms accurately predicted which participants would convert into the highest psychiatric illness risk group in the following year (area under the receiver operating characteristic curve = 0.84). The model trained solely on potential etiologies or disease mechanisms achieved an area under the receiver operating characteristic curve of 0.75 without relying on the child's current symptom burden. Sleep disturbances emerged as the most influential predictor of high-risk status, surpassing adverse childhood experiences and family mental health history. Including neuroimaging measures did not enhance predictive performance. These findings suggest that artificial intelligence models trained on readily available psychosocial questionnaires can effectively predict future psychiatric risk while highlighting potential targets for intervention. This is a promising step toward artificial intelligence-based mental health screening for clinical decision support systems.
© 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.
Conflict of interest statement
Competing interests: The authors declare no competing interests.
References
-
- Xiao Y, Brown TT, Snowden LR, Chow JC-C, Mann JJ. COVID-19 Policies, Pandemic Disruptions, and Changes in Child Mental Health and Sleep in the United States. JAMA Netw Open. 2023;6:e232716. - PubMed
-
- T K, R M, A H, E L, R A, C W, et al. Navigating inequities in the delivery of youth mental health care during the COVID-19 pandemic: perspectives of youth, families, and service providers. Can J Public Health Rev Can Sante Publique [Internet]. 2022. [cited 2024 Sep 3];113. Available from: https://pubmed.ncbi.nlm.nih.gov/35852728/ - PMC - PubMed
-
- Schmidhuber J Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117. - PubMed
METHODS-ONLY REFERENCES
-
- Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. Am J Prev Med. 1998;14:245–58. - PubMed
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