Speech-based identification of suicide risk in patients with MDD: model development and 4-week follow-up validation

Patients with major depressive disorder (MDD) have a substantially higher risk of suicide than the general population, whereas current suicide risk identification mainly relies on clinical interviews and standardized rating scales, with limited objectivity and capacity for continuous monitoring. Speech is a low-burden and repeatable digital behavioral signal that can provide acoustic and linguistic information related to emotional state, cognitive processing, and verbal expression, and may offer an objective adjunct for suicide risk identification in patients with MDD. Accordingly, this study aimed to evaluate whether speech-derived features could support suicide risk identification in patients with MDD by comparing the discriminative performance of different speech representations, examining the added value of clinical symptom measures, and assessing model performance at a 4-week follow-up.

Patients with MDD treated at Beijing Anding Hospital, Capital Medical University, with total scores of ≥14 on the 17-item Hamilton Depression Rating Scale (HAMD-17) were included. Suicide risk status was classified using a prespecified two-stage rule based on the HAMD-17 suicide item and the Beck Scale for Suicide Ideation-Chinese Version (BSI-CV). At baseline, clinical data and semi-structured interview speech were collected, and acoustic, traditional textual, and deep semantic features were extracted. Random forest models using different feature sources were developed and internally validated with fivefold stratified nested cross-validation. The textual-feature model and the model combining textual features with depressive symptom severity, represented by the Inventory of Depressive Symptomatology-Self-Report (IDS-SR) total score with the suicide-related item excluded, were further evaluated using 4-week follow-up data to determine whether their discriminative performance was maintained at a later assessment time point.

Among 248 patients with MDD, 92 were classified as high suicide risk and 156 as low suicide risk. In internal validation, the traditional textual-feature model achieved an area under the receiver operating characteristic curve (AUC) of 0.687, compared with AUCs of 0.604 and 0.641 for the acoustic- and deep semantic-feature models, respectively. The IDS-SR-only model achieved an AUC of 0.625. The models combining traditional textual features with the IDS-SR total score and with all clinical features achieved AUCs of 0.745 and 0.756, respectively. Among the 95 participants who completed the 4-week follow-up, 55 had a HAMD-17 total score of ≥14 at follow-up and were included in the 4-week follow-up validation analysis. In the 4-week follow-up validation set, the traditional textual-feature model achieved an AUC of 0.720, while the model combining traditional textual features with the IDS-SR total score achieved an AUC of 0.774.

Speech-derived textual features showed potential value for supporting suicide risk identification in patients with MDD. Combining speech-derived textual information with clinical symptom measures may provide complementary information beyond standard symptom assessment. These findings suggest that speech-based approaches may support clinicians in identifying suicide risk among patients with MDD, although further validation in larger independent multicenter cohorts is needed before clinical implementation.