Census-based adolescent depression risk assessment: A novel method and reflections
7 affiliations
- 1School of Physical Education, Shangrao Normal University, Shangrao, Jiangxi, China
- 2New York University, Graduate School of Arts and Science, New York, United States of America
- 3Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- 4School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- 5University of Michigan Ann Arbor, Ann Arbor, Michigan, United States of America
- 6Department of Informatics, King’s College London, London, United Kingdom
- 7Department of Kinesiology, University of Georgia, Athens, United States of America
Abstract
Diagnosing psychological issues in adolescents is generally more challenging than in adults. The need to enhance early identification and risk assessment for adolescent mental health problems has become particularly urgent, especially given the surge in such issues brought about by the COVID-19 pandemic and its isolation policies. Traditional approaches relying on scales or interviews can be difficult to administer at scale and may miss at-risk youths, while recent attempts at artificial intelligence–assisted clinical diagnosis have not yet achieved consistent performance for routine clinical use. Using youth census survey data from the National Survey of Children’s Health (NSCH) from 2020 to 2022 (147,772 samples), we propose a novel data synthesis and training management method tailored for imbalanced health data, and develop a heuristic decision model based on daily-life indicators to predict survey-reported adolescent depression risk. Experimental results demonstrate that our data management approach improves the average accuracy of conventional models by 14%. Compared with general artificial intelligence methods, the proposed decision model achieves higher accuracy, precision, and sensitivity. Built on AutoML, the model can adapt to factor replacement and incremental data updates, supporting scalable screening workflows and model maintenance. Using Shapley values, we identify several key daily-life factors associated with adolescent depression risk, such as social interactions and sleep duration. The interpretability analysis further supports the utility of our approach for risk stratification and practical decision support, rather than replacing clinician-administered diagnosis.
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BibTeX
@article{li2026censusbased,
title = {{Census-based adolescent depression risk assessment: A novel method and reflections}},
author = {Qiang Li and Yangxinrong Tang and Yiling Shi and Yufeng Wu and Zhan Xu and Hefeng Zhou and James J. Zhang},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357661},
url = {https://doi.org/10.1371/journal.pone.0357661},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Census-based adolescent depression risk assessment: A novel method and reflections
AU - Qiang Li
AU - Yangxinrong Tang
AU - Yiling Shi
AU - Yufeng Wu
AU - Zhan Xu
AU - Hefeng Zhou
AU - James J. Zhang
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0357661
UR - https://doi.org/10.1371/journal.pone.0357661
AB - Diagnosing psychological issues in adolescents is generally more challenging than in adults. The need to enhance early identification and risk assessment for adolescent mental health problems has become particularly urgent, especially given the surge in such issues brought about by the COVID-19 pandemic and its isolation policies. Traditional approaches relying on scales or interviews can be difficult to administer at scale and may miss at-risk youths, while recent attempts at artificial intelligence–assisted clinical diagnosis have not yet achieved consistent performance for routine clinical use. Using youth census survey data from the National Survey of Children’s Health (NSCH) from 2020 to 2022 (147,772 samples), we propose a novel data synthesis and training management method tailored for imbalanced health data, and develop a heuristic decision model based on daily-life indicators to predict survey-reported adolescent depression risk. Experimental results demonstrate that our data management approach improves the average accuracy of conventional models by 14%. Compared with general artificial intelligence methods, the proposed decision model achieves higher accuracy, precision, and sensitivity. Built on AutoML, the model can adapt to factor replacement and incremental data updates, supporting scalable screening workflows and model maintenance. Using Shapley values, we identify several key daily-life factors associated with adolescent depression risk, such as social interactions and sleep duration. The interpretability analysis further supports the utility of our approach for risk stratification and practical decision support, rather than replacing clinician-administered diagnosis.
SN - 1932-6203
ER -