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

ABSTRACT:

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 depress

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC12713288 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature