用深度学习模型识别文本中的自杀意念，准确率达93.97%。
Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanism
- 融合CNN、BiGRU与注意力机制，捕捉文本局部与序列特征。
- 在公开数据集上达到93.97%的分类准确率，优于现有方法。
- 结合SHAP解释性技术，让模型决策可理解，适合心理干预场景。
全球范围内，自杀是青少年的第二大死因，既往自杀行为是未来自杀风险的重要预测因子。尽管部分有自杀念头者会压抑情绪，但许多人在社交媒体中留下线索。为此，本文提出一种新型混合深度学习框架，将卷积神经网络（CNN）与双向门控循环单元（BiGRU）结合，从社交网络（SN）数据集中精准识别自杀意念模式。同时引入可解释人工智能方法——基于SHapley Additive exPlanations（SHAP）进行结果解释，验证模型可靠性。该框架整合了CNN的局部特征提取、BiGRU的双向序列建模、注意力机制及SHAP可解释性，构建了完整的自杀意念检测体系。系统在公开数据集上完成训练与评估，采用多种性能指标进行验证。实验结果显示，本方法准确率达到93.97%。与多种先进机器学习与深度学习模型及已有文献对比，表明本方法在各项指标上均显著优于竞争模型。
原文摘要 · Abstract (English)
Worldwide, suicide is the second leading cause of death for adolescents with past suicide attempts to be an important predictor for increased future suicides. While some people with suicidal thoughts may try to suppress them, many signal their intentions in social media platforms. To address these issues, we propose a new type of hybrid deep learning scheme, i.e., the combination of a CNN architecture and a BiGRU technique, which can accurately identify the patterns of suicidal ideation from SN datasets. Also, we apply Explainable AI methods using SHapley Additive exPlanations to interpret the prediction results and verifying the model reliability. This integration of CNN local feature extraction, BiGRU bidirectional sequence modeling, attention mechanisms, and SHAP interpretability provides a comprehensive framework for suicide detection. Training and evaluation of the system were performed on a publicly available dataset. Several performance metrics were used for evaluating model performance. Our method was found to have achieved 93.97 accuracy in experimental results. Comparative study to different state-of-the-art Machine Learning and DL models and existing literature demonstrates the superiority of our proposed technique over all the competing methods.
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