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已发表论文
利用机器学习和汉密尔顿抑郁量表 24 项版预测抑郁症患者的自杀意念
Authors Chen Y, Jiang ZY, Dong GZ, Zhang WY, Wang K, Yang HY
Received 29 April 2025
Accepted for publication 23 September 2025
Published 12 October 2025 Volume 2025:18 Pages 2153—2165
DOI https://doi.org/10.2147/PRBM.S537582
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Professor Einar Thorsteinsson
Yun Chen,1 Zhong-Yi Jiang,2 Guan-Zhong Dong,1 Wei-Yuan Zhang,1 Ke Wang,3 Hai-Yan Yang1
1Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China; 2School of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, 213000, People’s Republic of China; 3Nursing Teaching and Research Section, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China
Correspondence: Hai-Yan Yang, Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, No. 68 Ge Lake Middle Road, Wujin District, Changzhou, Jiangsu, 213000, People’s Republic of China, Tel +86 0519-81099988, Email yanghaiyanp2@126.com
Objective: The aim of this study was to identify factors associated with suicidal ideation and to develop a prediction model for early suicide ideation risk using machine learning algorithms based on the Hamilton Depression Scale (HAMD-24).
Methods: A total of 374 patients with depression were included from the outpatient department of the Psychology Department at the Second People’s Hospital of Changzhou City. Depression severity was assessed using the HAMD-24, while the Beck Suicide Ideation (BSI) Questionnaire (Chinese Version) was employed to categorize patients into those with and without suicidal ideation. Suicide ideation risk in patients with depression was predicted using four machine learning models: support vector machine, naive Bayes classification, random forest, and extreme random trees classification (ERTC). This superiority is attributed to ERTC’s extreme randomization which mitigates overfitting in high-dimensional symptom data. The models were evaluated based on accuracy, precision, recall, F1 scores, Kappa coefficients, Matthew’s correlation coefficients, and area under the curve values. The optimal model was then selected, and the factors most strongly associated with suicidal ideation using the HAMD-24 were identified and analyzed.
Results: The ERTC model outperformed SVM, NBC and RF (accuracy 77.75%, AUC 0.80), and despair, guilt, inferiority complex, work and interests loss, depression emotions were the strongest predictors of suicidal ideation. Demographically, patients with suicidal ideation were significantly younger and less likely to be using antidepressants. This is likely attributable to its ensemble structure and inherent randomization during node splitting, which enhances robustness against overfitting and improves generalization when handling the complex, potentially non-linear relationships between HAMD-24 items and suicidal ideation.
Conclusion: We identified the optimal model and then analyzed the factors most strongly associated with HAMD-24 suicidal ideation. The ERTC model, demonstrating superior performance, enables early interventions, and reduces suicide rates. Moreover, this model provides a theoretical reference for the development of new scales focused on depression and suicide.
Keywords: depression, machine learning, predictive model, suicidal ideation
1Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China; 2School of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, 213000, People’s Republic of China; 3Nursing Teaching and Research Section, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, Changzhou, Jiangsu, 213000, People’s Republic of China
Correspondence: Hai-Yan Yang, Department of Psychology, Nanjing Medical University Affiliated Changzhou Second People’s Hospital, No. 68 Ge Lake Middle Road, Wujin District, Changzhou, Jiangsu, 213000, People’s Republic of China, Tel +86 0519-81099988, Email yanghaiyanp2@126.com
Objective: The aim of this study was to identify factors associated with suicidal ideation and to develop a prediction model for early suicide ideation risk using machine learning algorithms based on the Hamilton Depression Scale (HAMD-24).
Methods: A total of 374 patients with depression were included from the outpatient department of the Psychology Department at the Second People’s Hospital of Changzhou City. Depression severity was assessed using the HAMD-24, while the Beck Suicide Ideation (BSI) Questionnaire (Chinese Version) was employed to categorize patients into those with and without suicidal ideation. Suicide ideation risk in patients with depression was predicted using four machine learning models: support vector machine, naive Bayes classification, random forest, and extreme random trees classification (ERTC). This superiority is attributed to ERTC’s extreme randomization which mitigates overfitting in high-dimensional symptom data. The models were evaluated based on accuracy, precision, recall, F1 scores, Kappa coefficients, Matthew’s correlation coefficients, and area under the curve values. The optimal model was then selected, and the factors most strongly associated with suicidal ideation using the HAMD-24 were identified and analyzed.
Results: The ERTC model outperformed SVM, NBC and RF (accuracy 77.75%, AUC 0.80), and despair, guilt, inferiority complex, work and interests loss, depression emotions were the strongest predictors of suicidal ideation. Demographically, patients with suicidal ideation were significantly younger and less likely to be using antidepressants. This is likely attributable to its ensemble structure and inherent randomization during node splitting, which enhances robustness against overfitting and improves generalization when handling the complex, potentially non-linear relationships between HAMD-24 items and suicidal ideation.
Conclusion: We identified the optimal model and then analyzed the factors most strongly associated with HAMD-24 suicidal ideation. The ERTC model, demonstrating superior performance, enables early interventions, and reduces suicide rates. Moreover, this model provides a theoretical reference for the development of new scales focused on depression and suicide.
Keywords: depression, machine learning, predictive model, suicidal ideation