120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
预测重度抑郁障碍患者自杀风险的机器学习模型建立与验证
Authors Tan H, Wang Z, Ma J, Wang Z, Zhang X
Received 18 August 2025
Accepted for publication 16 December 2025
Published 19 January 2026 Volume 2026:22 561526
DOI https://doi.org/10.2147/NDT.S561526
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Roger Pinder
Hang Tan,1 Zhanjin Wang,2 Jie Ma,1 Zhan Wang,2 Xiangyang Zhang3
1Department of Nursing, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China; 2Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China; 3Hefei Fourth People’s Hospital; Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University, Hefei, People’s Republic of China
Correspondence: Xiangyang Zhang, Hefei Fourth People’s Hospital, Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University, Hefei, People’s Republic of China, Email zhangxy@psych.ac.cn Zhan Wang, Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China, Email ufofu01@163.com
Background: Suicide, a serious outcome in major depressive disorder (MDD), necessitates early risk detection for clinical intervention. This study developed machine learning models to predict suicide risk in MDD patients.The model incorporated both psychological (eg,the Hamilton Anxiety Rating Scale[HAMA], the Clinical Global Impression of Severity Scale [CGI-S]) and biological (eg, thyroid-stimulating hormone [TSH], Systolic blood pressure[SBP]) predictors, with CGI-S, HAMA, and SBP emerging as the top predictors.
Patients and Methods: We analyzed data from 1,718 first-episode medicated patients with MDD recruited from the psychiatric outpatient department of the First Affiliated Hospital of Shanxi Medical University (March 2016–June 2017). Feature selection was performed using Least absolute shrinkage and selection operator (LASSO) regression, and the importance of the selected features was ranked using SHAP values via the XGBoost algorithm. The features were incrementally incorporated into the model construction based on their importance. Eleven machine-learning algorithms were evaluated, and an optimized stacked ensemble model was developed using a stacking algorithm. Model performance was assessed using Receiver Operating Characteristic Curve, precision-recall (PR) curves, accuracy, recall, and F1 scores. Interpretability was enhanced using kernel-SHAP and LIME algorithms.
Results: The Cohort comprised 1,718 MDD patients (mean age 34.87 ± 12.43 years; 34.20% male). Eight key predictors were selected: CGI-S score, HAMA score, TSH, SBP, PANSS positive subscale score, Antithyroglobulin, Diastolic blood pressure (DBP) and age. The top predictors, included CGI-S, HAMA, and SBP, aligning with pathways involving autonomic dysregulation and anxiety-depression interplay. The stacked ensemble model demonstrated superior performance, achieving an Area Under Curve of 0.868 and a PR value of 0.665 on the test set, outperforming all the other models. Decision curve analysis (DCA) confirmed its clinical utility, showing the highest net benefit across a risk threshold range of 0.03– 0.88. The SHAP method improved model interpretability and highlighted influential predictors.
Conclusion: The stacked ensemble model exhibited a strong predictive performance and clinical applicability for suicide risk assessment in patients with MDD. This tool may aid clinicians in the early identification and intervention of high-risk individuals and potentially improve patient outcomes.
Keywords: major depressive disorder, suicide attempt, machine learning, suicide prevention, predictive model
1Department of Nursing, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China; 2Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China; 3Hefei Fourth People’s Hospital; Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University, Hefei, People’s Republic of China
Correspondence: Xiangyang Zhang, Hefei Fourth People’s Hospital, Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University, Hefei, People’s Republic of China, Email zhangxy@psych.ac.cn Zhan Wang, Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qinghai University, Xining, People’s Republic of China, Email ufofu01@163.com
Background: Suicide, a serious outcome in major depressive disorder (MDD), necessitates early risk detection for clinical intervention. This study developed machine learning models to predict suicide risk in MDD patients.The model incorporated both psychological (eg,the Hamilton Anxiety Rating Scale[HAMA], the Clinical Global Impression of Severity Scale [CGI-S]) and biological (eg, thyroid-stimulating hormone [TSH], Systolic blood pressure[SBP]) predictors, with CGI-S, HAMA, and SBP emerging as the top predictors.
Patients and Methods: We analyzed data from 1,718 first-episode medicated patients with MDD recruited from the psychiatric outpatient department of the First Affiliated Hospital of Shanxi Medical University (March 2016–June 2017). Feature selection was performed using Least absolute shrinkage and selection operator (LASSO) regression, and the importance of the selected features was ranked using SHAP values via the XGBoost algorithm. The features were incrementally incorporated into the model construction based on their importance. Eleven machine-learning algorithms were evaluated, and an optimized stacked ensemble model was developed using a stacking algorithm. Model performance was assessed using Receiver Operating Characteristic Curve, precision-recall (PR) curves, accuracy, recall, and F1 scores. Interpretability was enhanced using kernel-SHAP and LIME algorithms.
Results: The Cohort comprised 1,718 MDD patients (mean age 34.87 ± 12.43 years; 34.20% male). Eight key predictors were selected: CGI-S score, HAMA score, TSH, SBP, PANSS positive subscale score, Antithyroglobulin, Diastolic blood pressure (DBP) and age. The top predictors, included CGI-S, HAMA, and SBP, aligning with pathways involving autonomic dysregulation and anxiety-depression interplay. The stacked ensemble model demonstrated superior performance, achieving an Area Under Curve of 0.868 and a PR value of 0.665 on the test set, outperforming all the other models. Decision curve analysis (DCA) confirmed its clinical utility, showing the highest net benefit across a risk threshold range of 0.03– 0.88. The SHAP method improved model interpretability and highlighted influential predictors.
Conclusion: The stacked ensemble model exhibited a strong predictive performance and clinical applicability for suicide risk assessment in patients with MDD. This tool may aid clinicians in the early identification and intervention of high-risk individuals and potentially improve patient outcomes.
Keywords: major depressive disorder, suicide attempt, machine learning, suicide prevention, predictive model