Analysis and Prediction of Relapse Risk Factors in Community-Dwelling Individuals With Schizophrenia: Retrospective Cohort Study.
6 affiliations
- 1Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, No.130, Dong'an Rd., Xuhui Dist., Shanghai, 200032, China, 86 13818782188.
- 2Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- 3Minhang District Mental Health Center of Shanghai, Shanghai, Shanghai, China.
- 4School of Art, Design and Media, East China University of Science and Technology, Shanghai, China.
- 5Department of Maternal, Child and Adolescent Health, School of Public Health, Fudan University, Shanghai, China.
- 6School of Design, Shanghai Jiao Tong University, Shanghai, China.
Abstract
Background
Schizophrenia is a severe mental disorder characterized by high disability and relapse rates, posing significant challenges to public safety and community-based mental health management.Objective
This study aimed to develop and validate a model for identifying hospitalization-related severe relapse risk among individuals with schizophrenia and to determine the key factors associated with this risk.Methods
A total of 44,804 electronic records of community-managed individuals with schizophrenia were extracted from an information management system between January 1, 2022, and December 31, 2023. The dataset included demographic information, clinical records, interview data, policy support, and management indicators to construct a comprehensive feature system. A Light Gradient Boosting Machine (LightGBM) machine learning algorithm was used for model development, with cost-sensitive learning applied to address class imbalance. Model interpretability was enhanced using Shapley additive explanations (SHAP) to identify the most influential predictors of relapse risk.Results
After screening, a total of 39,818 community-dwelling individuals with schizophrenia were included in this study, of whom 3717 (9.33%) experienced hospitalization-related severe relapse. The dataset was randomly divided using stratified sampling into a training set (n=31,854, 80%) and an independent test set (n=7964, 20%). Among the 6 machine learning models evaluated, LightGBM showed leading and balanced performance, yielding an area under the receiver operating characteristic curve of 0.95, an area under the precision-recall curve of 0.79, a relapse-class recall of 0.77, a precision of 0.71, and an F₁-score of 0.74 in the independent test set. SHAP-based interpretability analysis identified medication persistence (23.30%), specialist recommendation: medication adherence training (19.74%), number of follow-ups (7.38%), age (3.72%), and educational attainment (3.58%) as the most influential predictors. Higher hospitalization-related severe relapse risk was generally associated with interrupted or refused medication use, more frequent follow-ups, younger age, and higher educational attainment, whereas sustained medication use, better occupational functioning and productive work, greater social participation, and better social functioning were associated with lower predicted risk.Conclusions
This study developed a hospitalization-related severe relapse risk identification model that integrates multidimensional factors reflecting the clinical and functional complexity of schizophrenia. The findings provide an evidence-based foundation for risk identification, targeted intervention, and the optimization of community mental health services to reduce hospitalization-related severe relapse risk and support long-term recovery.
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BibTeX
@article{zhang2026analysis,
title = {{Analysis and Prediction of Relapse Risk Factors in Community-Dwelling Individuals With Schizophrenia: Retrospective Cohort Study.}},
author = {Weibo Zhang and Dian Zhu and Xiaolan Wang and Ruilan Li and Hong Jiang and Yanli Liu and Jun Cai and Zhao Liu and Ling Wang},
journal = {JMIR medical informatics},
year = {2026},
month = oct,
doi = {10.2196/86991},
url = {https://europepmc.org/article/MED/42950201},
note = {CC BY},
}
RIS
TY - JOUR
TI - Analysis and Prediction of Relapse Risk Factors in Community-Dwelling Individuals With Schizophrenia: Retrospective Cohort Study.
AU - Weibo Zhang
AU - Dian Zhu
AU - Xiaolan Wang
AU - Ruilan Li
AU - Hong Jiang
AU - Yanli Liu
AU - Jun Cai
AU - Zhao Liu
AU - Ling Wang
PY - 2026
DA - 2026/10/01
JO - JMIR medical informatics
DO - 10.2196/86991
UR - https://europepmc.org/article/MED/42950201
AB - Background Schizophrenia is a severe mental disorder characterized by high disability and relapse rates, posing significant challenges to public safety and community-based mental health management.Objective This study aimed to develop and validate a model for identifying hospitalization-related severe relapse risk among individuals with schizophrenia and to determine the key factors associated with this risk.Methods A total of 44,804 electronic records of community-managed individuals with schizophrenia were extracted from an information management system between January 1, 2022, and December 31, 2023. The dataset included demographic information, clinical records, interview data, policy support, and management indicators to construct a comprehensive feature system. A Light Gradient Boosting Machine (LightGBM) machine learning algorithm was used for model development, with cost-sensitive learning applied to address class imbalance. Model interpretability was enhanced using Shapley additive explanations (SHAP) to identify the most influential predictors of relapse risk.Results After screening, a total of 39,818 community-dwelling individuals with schizophrenia were included in this study, of whom 3717 (9.33%) experienced hospitalization-related severe relapse. The dataset was randomly divided using stratified sampling into a training set (n=31,854, 80%) and an independent test set (n=7964, 20%). Among the 6 machine learning models evaluated, LightGBM showed leading and balanced performance, yielding an area under the receiver operating characteristic curve of 0.95, an area under the precision-recall curve of 0.79, a relapse-class recall of 0.77, a precision of 0.71, and an F₁-score of 0.74 in the independent test set. SHAP-based interpretability analysis identified medication persistence (23.30%), specialist recommendation: medication adherence training (19.74%), number of follow-ups (7.38%), age (3.72%), and educational attainment (3.58%) as the most influential predictors. Higher hospitalization-related severe relapse risk was generally associated with interrupted or refused medication use, more frequent follow-ups, younger age, and higher educational attainment, whereas sustained medication use, better occupational functioning and productive work, greater social participation, and better social functioning were associated with lower predicted risk.Conclusions This study developed a hospitalization-related severe relapse risk identification model that integrates multidimensional factors reflecting the clinical and functional complexity of schizophrenia. The findings provide an evidence-based foundation for risk identification, targeted intervention, and the optimization of community mental health services to reduce hospitalization-related severe relapse risk and support long-term recovery.
SN - 2291-9694
ER -