# Probabilistic Textual Time Series Depression Detection

arXiv: https://arxiv.org/abs/2511.04476 · HTML: https://mlsanity.com/paper/2511.04476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Multi-level Attention network using text, audio and video for Depression Prediction](https://mlsanity.com/paper/1909.01417/) (2019)
- [Depression Severity Estimation from Multiple Modalities](https://mlsanity.com/paper/1711.06095/) (2017)
- [A Topic-Attentive Transformer-based Model For Multimodal Depression Detection](https://mlsanity.com/paper/2206.13256/) (2022)
- [Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews](https://mlsanity.com/paper/2307.00920/) (2023)
- [The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level](https://mlsanity.com/paper/1904.07656/) (2019)
- [Enhancing Depression Detection via Question-wise Modality Fusion](https://mlsanity.com/paper/2503.20496/) (2025)
- [Fair Uncertainty Quantification for Depression Prediction](https://mlsanity.com/paper/2505.04931/) (2025)

## Built on by (most cited first, 1 of 1)

- [Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue](https://mlsanity.com/paper/2606.17973/) (2026)

## Abstract (arXiv)

Accurate and interpretable predictions of depression severity are essential for clinical decision support, yet existing models often lack uncertainty estimates and temporal interpretability. We propose PTTSD, a Probabilistic framework for Depression Detection from clinical interview utterance sequences that predicts PHQ-8 scores while modeling calibrated uncertainty. PTTSD includes sequence-to-sequence and sequence-to-one variants, both combining LSTMs, self-attention, and residual connections with Gaussian or Student's-t output heads trained via negative log-likelihood. The sequence-to-sequence variant enables temporal analysis of how predictive confidence evolves over an interview, despite the target being a single session-level score. Evaluated on E-DAIC and DAIC-WOZ, PTTSD achieves competitive 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 a three-part calibration analysis and qualitative case studies highlight the clinical relevance of uncertainty-aware prediction.