Probabilistic Textual Time Series Depression Detection

2026-07-13 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Mental Health & Psychological Support · Depth: Expert, extended

Summary

The Probabilistic Textual Time Series Depression Detection (PTTSD) framework is introduced for predicting PHQ-8 depression severity from utterance-level clinical interviews, uniquely modeling uncertainty over time. PTTSD employs sequence-to-sequence and sequence-to-one variants, integrating bidirectional LSTMs, self-attention, and residual connections with Gaussian or Student's-t output heads trained via negative log-likelihood. Evaluated on E-DAIC and DAIC-WOZ datasets, PTTSD achieves leading performance among text-only systems, with MAE of 3.85 on E-DAIC and 3.55 on DAIC, and produces well-calibrated prediction intervals. Ablation studies confirm the value of attention and probabilistic modeling, highlighting its interpretability and clinical relevance for uncertainty-aware forecasting.

Key takeaway

For AI Scientists developing clinical decision support systems, this work demonstrates that incorporating probabilistic modeling into textual time series analysis is crucial. You should prioritize frameworks like PTTSD that provide calibrated uncertainty estimates alongside predictions, as this enhances interpretability and trustworthiness. This approach allows you to adapt confidence based on input ambiguity, making your models more actionable for mental health screening and triage.

Key insights

PTTSD provides calibrated uncertainty estimates for depression severity prediction from textual time series, crucial for clinical NLP.

Principles

• Uncertainty quantification is vital for high-stakes clinical NLP.
• Self-attention and residual connections significantly improve model performance.
• Gaussian NLL offers reliable performance for uncertainty modeling.

Method

PTTSD encodes utterance sequences via BiLSTMs, self-attention, and residual connections, then uses Gaussian or Student's-t heads to predict PHQ-8 scores and calibrated uncertainty via negative log-likelihood.

In practice

• Use WhisperX for improved transcription fidelity in clinical NLP.
• Implement probabilistic output heads for uncertainty-aware predictions.
• Evaluate calibration using ECE and coverage plots for clinical relevance.

Topics

• Depression Detection
• Clinical NLP
• Probabilistic Modeling
• Uncertainty Quantification
• Textual Time Series
• PHQ-8 Assessment

Code references

• someonedoing-research/PTTSD

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.