Mobile Doctoral Station, School of Nursing, Chongqing Medical University, Chongqing, China.
Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
J Affect Disord. 2021 Feb 1;280(Pt A):72-76. doi: 10.1016/j.jad.2020.10.077. Epub 2020 Nov 5.
Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD), especially among adolescents and young adults. Machine learning models with the capability of suicide risk identification at an individual level could improve suicide prevention among high-risk patient population.
A cross-sectional assessment was conducted on a sample of 66 adolescents/young adults diagnosed with MDD. The structural T1-weighted MRI scan of each subject was processed using the FreeSurfer software. The classification model was conducted using the Support Vector Machine - Recursive Feature Elimination (SVM-RFE) algorithm to distinguish suicide attempters and patients with suicidal ideation but without attempts.
The SVM model was able to correctly identify suicide attempters and patients with suicidal ideation but without attempts with a cross-validated prediction balanced accuracy of 78.59%, the sensitivity was 73.17% and the specificity was 84.0%. The positive predictive value of suicide attempt was 88.24%, and the negative predictive value was 65.63%. Right lateral orbitofrontal thickness, left caudal anterior cingulate thickness, left fusiform thickness, left temporal pole volume, right rostral anterior cingulate volume, left lateral orbitofrontal thickness, left posterior cingulate thickness, right pars orbitalis thickness, right posterior cingulate thickness, and left medial orbitofrontal thickness were the 10 top-ranked classifiers for suicide attempt.
The findings indicated that structural MRI data can be useful for the classification of suicide risk. The algorithm developed in current study may lead to identify suicide attempt risk among MDD patients.
自杀行为是患有重度抑郁症（MDD）患者的主要关注点，尤其是在青少年和年轻成年人中。具有个体自杀风险识别能力的机器学习模型可以提高高危患者人群的预防自杀效果。
对 66 名被诊断患有 MDD 的青少年/年轻成年人进行了横断面评估。使用 FreeSurfer 软件对每个受试者的结构 T1 加权 MRI 扫描进行处理。使用支持向量机-递归特征消除（SVM-RFE）算法进行分类模型，以区分自杀未遂者和有自杀意念但无自杀企图的患者。
SVM 模型能够正确识别自杀未遂者和有自杀意念但无自杀企图的患者，其交叉验证预测平衡准确率为 78.59%，敏感性为 73.17%，特异性为 84.0%。自杀企图的阳性预测值为 88.24%，阴性预测值为 65.63%。右侧外侧眶额皮质厚度、左侧后扣带回前部皮质厚度、左侧梭状回厚度、左侧颞极体积、右侧额极前部皮质体积、左侧外侧眶额皮质厚度、左侧后扣带回皮质厚度、右侧眶额眶部厚度、右侧后扣带回皮质厚度和左侧内侧眶额皮质厚度是 10 个预测自杀企图的最佳分类器。
研究结果表明，结构 MRI 数据可用于自杀风险的分类。本研究开发的算法可用于识别 MDD 患者的自杀未遂风险。
