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Background <p>Suicidal ideation in depression is a critical predictor of suicide risk, yet its objective and early identification remains a significant challenge. Current machine learning models often fail to distinguish specific markers of suicidality from the general symptoms of severe depression. This study aimed to develop and validate a multimodal model, integrating vocal features and autobiographical memory, to specifically distinguish depressed patients with suicidal ideation from those without.</p> Methods <p>This study enrolled 88 patients with depression, who were divided into three groups based on depression severity and the presence of suicidal ideation: mild depression without suicidal ideation(mD-NSI), moderate depression with suicidal ideation(MD-SI), and severe depression with suicidal ideation(SD-SI). Methodologies included the Autobiographical Memory Test (AMT), clinical scales (BDI-II, OGMQ), and comprehensive vocal feature extraction. We used repeated measures analysis of variance (ANOVA) for group comparisons and developed machine learning models, primarily Random Forest, for various classification tasks.</p> Results <p>Significant differences were found in autobiographical memory; patients with suicidal ideation demonstrated significant overgeneralization, retrieving fewer specific memories than those without. Acoustically, individuals with suicidal ideation exhibited distinct vocal patterns, including reduced prosodic variation and altered spectral energy, indicated by features like Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, and zero-crossing rate. A Random Forest model achieved high accuracy (AUC up to 1.00) in classification. Crucially, model interpretability analysis (SHAP) revealed that the predictive importance of features shifted depending on the clinical comparison: autobiographical memory scores were key for distinguishing the initial presence of suicidal ideation, whereas depression severity scores became more prominent when differentiating between moderate and severe cases who were already suicidal.</p> Conclusion <p>The integrated analysis of vocal features and autobiographical memory, validated through an interpretable machine learning model, offers a powerful and objective approach for predicting suicidal ideation in depression. This multimodal method not only effectively differentiates patients with and without suicidal ideation but also provides novel insights into the shifting cognitive and physiological markers of suicide risk. It represents a significant step towards developing precise, clinically applicable tools for early risk identification and timely intervention.</p> Clinical trial number <p>Not applicable.</p>

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Speech feature identification model for depressed individuals with suicidal ideation based on autobiographical memory

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Ying Zhu,
  • Qianlan Yin,
  • Huijing Xu,
  • Fang Xiao,
  • Qian Jiang,
  • Meng Liang,
  • Qi Cheng,
  • Taosheng Liu

摘要

Background

Suicidal ideation in depression is a critical predictor of suicide risk, yet its objective and early identification remains a significant challenge. Current machine learning models often fail to distinguish specific markers of suicidality from the general symptoms of severe depression. This study aimed to develop and validate a multimodal model, integrating vocal features and autobiographical memory, to specifically distinguish depressed patients with suicidal ideation from those without.

Methods

This study enrolled 88 patients with depression, who were divided into three groups based on depression severity and the presence of suicidal ideation: mild depression without suicidal ideation(mD-NSI), moderate depression with suicidal ideation(MD-SI), and severe depression with suicidal ideation(SD-SI). Methodologies included the Autobiographical Memory Test (AMT), clinical scales (BDI-II, OGMQ), and comprehensive vocal feature extraction. We used repeated measures analysis of variance (ANOVA) for group comparisons and developed machine learning models, primarily Random Forest, for various classification tasks.

Results

Significant differences were found in autobiographical memory; patients with suicidal ideation demonstrated significant overgeneralization, retrieving fewer specific memories than those without. Acoustically, individuals with suicidal ideation exhibited distinct vocal patterns, including reduced prosodic variation and altered spectral energy, indicated by features like Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, and zero-crossing rate. A Random Forest model achieved high accuracy (AUC up to 1.00) in classification. Crucially, model interpretability analysis (SHAP) revealed that the predictive importance of features shifted depending on the clinical comparison: autobiographical memory scores were key for distinguishing the initial presence of suicidal ideation, whereas depression severity scores became more prominent when differentiating between moderate and severe cases who were already suicidal.

Conclusion

The integrated analysis of vocal features and autobiographical memory, validated through an interpretable machine learning model, offers a powerful and objective approach for predicting suicidal ideation in depression. This multimodal method not only effectively differentiates patients with and without suicidal ideation but also provides novel insights into the shifting cognitive and physiological markers of suicide risk. It represents a significant step towards developing precise, clinically applicable tools for early risk identification and timely intervention.

Clinical trial number

Not applicable.