E-DAIC
PulseAugur coverage of E-DAIC — every cluster mentioning E-DAIC across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New AI framework estimates depression severity with uncertainty quantification
Researchers have developed EviDep, a novel framework for estimating depression severity using audio-visual data. This system employs evidential learning to quantify both aleatoric and epistemic uncertainty in its predic…
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New Hierarchical Multi-Agent System Enhances Depression Detection
Researchers have developed HiMA-MDD, a novel hierarchical multi-agent system designed for interpretable multimodal depression detection in clinical interviews. This system organizes evidence gathering and assessment int…
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New AI Model Predicts Depression Severity with Uncertainty Estimates
Researchers have developed PTTSD, a novel probabilistic framework designed to detect depression severity from clinical interview transcripts. This system utilizes LSTMs and self-attention mechanisms to predict PHQ-8 sco…
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New MA-DLE method estimates depression levels from speech
Researchers have developed a new method called MA-DLE for estimating depression levels using speech analysis. This approach augments standard GRU-extracted features with a memory bank that selectively integrates histori…
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Dep-LLM uses LLMs for training-free depression diagnosis
Researchers have developed Dep-LLM, a novel framework for diagnosing depression from clinical interviews without requiring any additional training. This system leverages existing large language models (LLMs) by mimickin…
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FAIR_XAI framework reveals bias in multimodal models for wellbeing assessment
Researchers have developed FAIR_XAI, a framework to improve the fairness of multimodal foundation models used in wellbeing assessment. The study evaluated Phi3.5-Vision and Qwen2-VL on datasets like E-DAIC and AFAR-BSFT…
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New PsyGAT model achieves SOTA in depression detection, outperforming GPT-5
Researchers have developed PsyGAT, a novel graph-based framework for detecting depression from conversational data. This model addresses data scarcity and interpretability issues common in existing deep learning approac…