How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.

中文翻译：

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通过机器学习识别重度抑郁症自杀未遂者：避免疼痛的重要性、情感处理的事件相关潜在特征

如何实现基于三维心理疼痛模型的高精度自杀企图分类器是自杀研究中的一个有价值的问题。本研究的目的是探讨重度抑郁症患者自杀未遂分类模型中避免疼痛及其相关神经特征的重要性。通过交叉验证和支持向量机算法的递归特征消除，选择测量分数和基于任务的脑电图信号来实现自杀企图分类模型。在多模态自杀未遂分类器中，疼痛回避在准确度为 83.91%、曲线下面积为 0.90 的最佳特征集中排名第一。 Theta（奖励正反馈减去中性正反馈）是共享神经表征，在疼痛避免和自杀企图分类器中排名第一的事件相关潜在特征。总之，基于疼痛回避及其相关情感处理神经特征的自杀企图分类器在重度抑郁症患者中具有出色的准确性。在传统分析和机器学习方法中，避免疼痛是识别自杀风险的稳定而有力的指标。需要一种新的方法来阐明惩罚刺激和避免疼痛引起的认知和情感处理之间的关系。