Predicting Depressive Symptoms From Cross-Sectional and Longitudinal Data Using XGBoost-SHAP and Cross-Lagged Model.
- 1State Key Laboratory of Digital Medical Engineering Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical Engineering Hainan University Sanya Hainan 572024 China hainu.edu.cn.
- 2School of Biomedical Engineering Sichuan University Chengdu China scu.edu.cn.
Abstract
Objective
University students face significant academic, social, and career-planning challenges. Under such stressors, deficient emotion regulation may increase vulnerability to depressive states, in which persistent symptoms could progress to clinical depression. This study investigates key risk and protective factors for depressive symptoms among university students, including negative life events, neuroticism, and social support. A longitudinal framework was further employed to assess changes in depressive symptoms before and after the COVID-19 pandemic, with the goal of informing early identification and intervention strategies.Methods
The dataset included 18 024 valid responses from baseline assessments. Follow-up assessments conducted 1 year later included 6129 participants. The extreme gradient boosting (XGBoost) model with SHapley Additive exPlanations (SHAP) was used to quantify the relative importance of specific factors (e.g., negative life events and social support) on depressive symptoms, while a cross-lagged panel model examined longitudinal directional associations between these factors and depressive symptoms.Results
The prevalence of depressive symptoms was 15.2% among the student sample. Significant correlations were observed between symptom severity and adverse family relationships, childhood trauma, prior suicide attempts, and psychiatric history. Neuroticism and negative life events showed significant positive associations with depressive symptoms, while subjective support and support utilization demonstrated negative associations with symptom severity. Longitudinal analysis revealed an increase in both symptom severity and depression detection rates following the COVID-19 pandemic. The cross-lagged panel model identified neuroticism as a significant positive longitudinal predictor of depressive symptoms, whereas subjective support showed a significant negative longitudinal association with subsequent depressive symptoms.Conclusion
This study identifies neuroticism as an important longitudinal risk indicator for depressive symptoms and subjective support as a protective indicator, while negative life events showed significant cross-sectional associations with symptom severity. The findings demonstrate how integrating longitudinal designs with interpretable machine learning elucidates complex mental health dynamics in student populations, providing an evidence-based framework for targeted interventions within campus mental health policies.
Related
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- Associations of pandemic-era telework with cognition and depressive symptoms among middle-aged and older nonagricultural workers in China: a longitudinal CHARLS study
- Sleep duration, depressive symptoms, and memory aging: longitudinal patterns and temporal network evidence from four-wave CHARLS study (2011–2018)
- Joint trajectories and associated characteristics of postpartum depressive and anxiety symptoms: a longitudinal study
BibTeX
@article{jie2026predicting,
title = {{Predicting Depressive Symptoms From Cross-Sectional and Longitudinal Data Using XGBoost-SHAP and Cross-Lagged Model.}},
author = {Jing Jie and Ming Yin and Jiayue Zeng and Songlin Yang},
journal = {Depression and anxiety},
year = {2026},
month = oct,
doi = {10.1155/da/4401725},
url = {https://europepmc.org/article/MED/42948234},
note = {CC BY},
}
RIS
TY - JOUR
TI - Predicting Depressive Symptoms From Cross-Sectional and Longitudinal Data Using XGBoost-SHAP and Cross-Lagged Model.
AU - Jing Jie
AU - Ming Yin
AU - Jiayue Zeng
AU - Songlin Yang
PY - 2026
DA - 2026/10/27
JO - Depression and anxiety
DO - 10.1155/da/4401725
UR - https://europepmc.org/article/MED/42948234
AB - Objective University students face significant academic, social, and career-planning challenges. Under such stressors, deficient emotion regulation may increase vulnerability to depressive states, in which persistent symptoms could progress to clinical depression. This study investigates key risk and protective factors for depressive symptoms among university students, including negative life events, neuroticism, and social support. A longitudinal framework was further employed to assess changes in depressive symptoms before and after the COVID-19 pandemic, with the goal of informing early identification and intervention strategies.Methods The dataset included 18 024 valid responses from baseline assessments. Follow-up assessments conducted 1 year later included 6129 participants. The extreme gradient boosting (XGBoost) model with SHapley Additive exPlanations (SHAP) was used to quantify the relative importance of specific factors (e.g., negative life events and social support) on depressive symptoms, while a cross-lagged panel model examined longitudinal directional associations between these factors and depressive symptoms.Results The prevalence of depressive symptoms was 15.2% among the student sample. Significant correlations were observed between symptom severity and adverse family relationships, childhood trauma, prior suicide attempts, and psychiatric history. Neuroticism and negative life events showed significant positive associations with depressive symptoms, while subjective support and support utilization demonstrated negative associations with symptom severity. Longitudinal analysis revealed an increase in both symptom severity and depression detection rates following the COVID-19 pandemic. The cross-lagged panel model identified neuroticism as a significant positive longitudinal predictor of depressive symptoms, whereas subjective support showed a significant negative longitudinal association with subsequent depressive symptoms.Conclusion This study identifies neuroticism as an important longitudinal risk indicator for depressive symptoms and subjective support as a protective indicator, while negative life events showed significant cross-sectional associations with symptom severity. The findings demonstrate how integrating longitudinal designs with interpretable machine learning elucidates complex mental health dynamics in student populations, providing an evidence-based framework for targeted interventions within campus mental health policies.
SN - 1091-4269
SN - 1520-6394
ER -