Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Development and validation of a machine learning-based diagnostic model for depression in young populations: a retrospective cohort study
Related
- Development and validation of a clinical prediction model for diagnosing nephrotic syndrome in adults: a retrospective diagnostic prediction study
- Development and Internal Validation of an Interpretable Machine Learning Model for Identifying Past-Year Nonsuicidal Self-Injury Among Adolescents With Depression: Retrospective Study.
- Psychometric validation of the Depression Stigmatization Scale in Kazakh- and Russian-speaking populations
- Development and Internal Validation of Multivariable Prediction Models for Depression, Anxiety, and Stress Among Saudi Adolescent Video Game Players: A Cross-Sectional Study
- Polygenic Prediction of Depression Liability Reveals Broad Genetic Associations Across Development, Families, and Populations
BibTeX
@article{shi2026development,
title = {{Development and validation of a machine learning-based diagnostic model for depression in young populations: a retrospective cohort study}},
author = {Ke Shi and Yangyi Guo and Tongdeng You},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76338-7},
url = {https://www.nature.com/articles/s41598-026-76338-7},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Development and validation of a machine learning-based diagnostic model for depression in young populations: a retrospective cohort study
AU - Ke Shi
AU - Yangyi Guo
AU - Tongdeng You
PY - 2026
DA - 2026/10/21
JO - Scientific Reports
DO - 10.1038/s41598-026-76338-7
UR - https://www.nature.com/articles/s41598-026-76338-7
SN - 2045-2322
ER -