Probabilistic Textual Time Series Depression Detection

• URL: http://arxiv.org/abs/2511.04476v1
• Date: Thu, 06 Nov 2025 15:50:33 GMT
• Title: Probabilistic Textual Time Series Depression Detection
• Abstract summary: PTTSD predicts PHQ-8 scores from utterance-level clinical interviews while modeling uncertainty over time.<n>PTTSD achieves state-of-the-art performance among text-only systems.
• Score: 0.4164728134421114
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and interpretable predictions of depression severity are essential for clinical decision support, yet existing models often lack uncertainty estimates and temporal modeling. We propose PTTSD, a Probabilistic Textual Time Series Depression Detection framework that predicts PHQ-8 scores from utterance-level clinical interviews while modeling uncertainty over time. PTTSD includes sequence-to-sequence and sequence-to-one variants, both combining bidirectional LSTMs, self-attention, and residual connections with Gaussian or Student-t output heads trained via negative log-likelihood. Evaluated on E-DAIC and DAIC-WOZ, PTTSD achieves state-of-the-art performance among text-only systems (e.g., MAE = 3.85 on E-DAIC, 3.55 on DAIC) and produces well-calibrated prediction intervals. Ablations confirm the value of attention and probabilistic modeling, while comparisons with MentalBERT establish generality. A three-part calibration analysis and qualitative case studies further highlight the interpretability and clinical relevance of uncertainty-aware forecasting.

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