Abstract
The delayed access to specialized psychiatric assessments and care for patients at risk of suicidal tendencies in emergency departments creates a notable gap in timely intervention, hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive, speech-based approach for automatic suicide risk assessment. For our study, we have collected a novel dataset of speech recordings from $20$ patients from which we extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of $66.2\,\%$. Moreover, we show that integrating our speech model with a series of patients' metadata, such as the history of suicide attempts or access to firearms, improves the overall result. The metadata integration yields a balanced accuracy of $94.4\,\%$, marking an absolute improvement of $28.2\,\%$, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.
Abstract (translated)
延迟在急诊科接受专业精神科评估和治疗患者存在自杀倾向， creates a notable gap in timely intervention， hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive，speech-based approach for automatic suicide risk assessment. For our study， we have collected a novel dataset of speech recordings from $20$ patients from which we extract three sets of features，including wav2vec，interpretable speech and acoustic features， and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of $66.2\%$．此外，我们证明了将我们的语音模型与一系列患者元数据（如自杀尝试历史或获取枪支的渠道）集成可以提高整体结果。元数据集成使平衡准确率达到了$94.4\%$，表明我们在急诊医学中自动自杀风险评估方法的实效性。
URL
https://arxiv.org/abs/2404.12132
