College of Information Engineering, Tianjin University of Commerce, Tianjin, China.
College of Sciences, Tianjin University of Commerce, Tianjin, China.
PeerJ. 2023 Nov 8;11:e16362. doi: 10.7717/peerj.16362. eCollection 2023.
Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts.
Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not.
The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model.
This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts.
有自杀企图的重性抑郁障碍（MDD）患者已成为全球精神卫生领域的重要挑战。为了正确区分有和无自杀企图的 MDD 患者，本研究采用多模态数据（包括人口统计学、抑郁症状和脑结构影像学数据）开发了一种多模态预测模型。该模型将非常有助于有自杀企图的 MDD 患者的早期干预。
本研究采用支持向量机递归特征消除（SVM-RFE）和随机森林（RF）算法融合的两种特征选择方法对 208 例 MDD 患者进行特征选择。然后，SVM 被用作分类模型来区分是否有自杀企图的 MDD 患者。
使用 SVM-RFE 和 RF 集成特征的多模态预测模型能够正确区分有和无自杀企图的 MDD 患者，均衡准确率为 77.78%，敏感度为 83.33%，特异度为 70.37%，阳性预测值为 78.95%，阴性预测值为 76.00%。与传统方法相比，合并两种选择方法特征的策略在预测 MDD 患者自杀企图方面表现更好，预测模型中排名前三的特征是海马体积、小脑蚓部体积和顶内沟体积。
本研究不仅开发了一种新的多模态预测模型，还发现了三种预测 MDD 患者自杀企图的重要脑结构表型。该预测模型是对 MDD 患者进行早期干预的有力工具，为有自杀企图的 MDD 患者的治疗提供了神经影像学生物标志物靶点。
