逻辑回归
自杀意念
精神分裂症(面向对象编程)
决策树模型
临床心理学
心理学
精神科
心理弹性
医学
决策树
毒物控制
伤害预防
内科学
医疗急救
数据挖掘
计算机科学
心理治疗师
作者
Hong Yu,Yujing Sun,Jiaxin Ren,Mengnan Qin,Hong Su,Yuqiu Zhou,Dongyu Hou,W. J. Zhang
标识
DOI:10.1080/13548506.2023.2301225
摘要
This study aimed to investigate the factors associated with suicidal ideation in schizophrenia patients in China using decision tree and logistic regression models. From October 2020 to March 2022, patients with schizophrenia were chosen from Chifeng Anding Hospital and Daqing Third Hospital in Heilongjiang Province. A total of 300 patients with schizophrenia who met the inclusion criteria were investigated by questionnaire. The questionnaire covered general data, suicidal ideation, childhood trauma, social support, depressive symptoms and psychological resilience. Logistic regression analysis revealed that childhood trauma and depressive symptoms were risk factors for suicidal ideation in schizophrenia (OR = 2.330, 95%CI: 1.177 ~ 4.614; OR = 10.619, 95%CI: 5.199 ~ 21.688), while psychological resilience was a protective factor for suicidal ideation in schizophrenia (OR = 0.173, 95%CI: 0.073 ~ 0.409). The results of the decision tree model analysis demonstrated that depressive symptoms, psychological resilience and childhood trauma were influential factors for suicidal ideation in patients with schizophrenia (p < 0.05). The area under the ROC for the logistic regression model and the decision tree model were 0.868 (95% CI: 0.821 ~ 0.916) and 0.863 (95% CI: 0.814 ~ 0.912) respectively, indicating excellent accuracy of the models. Meanwhile, the logistic regression model had a sensitivity of 0.834 and a specificity of 0.743 when the Youden index was at its maximum. The decision tree model had a sensitivity of 0.768 and a specificity of 0.8. Decision trees in combination with logistic regression models are of high value in the study of factors influencing suicidal ideation in schizophrenia patients.
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