ArticleScientific reports2024
Prediction of adolescent depression from prenatal and childhood data from ALSPAC using machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Predicting adolescent depression: an interpretable machine learning model.BMC medical informatics and decision making · 2026Article
- Using Machine Learning to Predict Depression among Adolescents Living with HIV in Uganda.Global social welfare : research, policy & practice · 2026Article
- Artificial intelligence as decision support for adolescent depression and anxiety: a mini review of clinical utility, safety, and implementation.Frontiers in psychiatry · 2026Review
- The risk factors for the comorbidity of depression and self-injury in adolescents: a machine learning study.European child & adolescent psychiatry · 2025Article
- Longitudinal Prediction of Adolescent Depression from Environmental and Polygenic Risk Scores.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child's prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness.
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Registered trials
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