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This paper introduces Score-Guided Classification (SGC), a framework that models pathological priors using an unsupervised generative network for EEG-based depression detection, avoiding synthetic data augmentation and improving classification accuracy.
This paper presents a hybrid model combining DistilBERT embeddings with Holographic Reduced Representation vectors encoding cognitive-linguistic features (first-person pronouns, absolutist words, negative emotion ratios) to detect depression in Reddit posts, achieving a macro F1 of 0.94 and demonstrating that theory-driven features complement contextual embeddings for explainable mental health NLP.
This paper compares several post-hoc explainability methods applied to an InceptionTime model for EEG-based depression detection, finding partial convergence among methods while highlighting methodological variability and limitations.
This paper audits benchmark evaluation in clinical-interview depression detection through four complementary probes across five datasets, finding that standard evaluation protocols may overestimate model performance and that leaderboard rankings lack stability.
Proposes an agentic framework using LangChain agents for population-scale mental health screening, focusing on depression detection from clinical transcripts. The framework incrementally locks validated stages and uses proxy-guided evaluation to ensure trustworthiness and adaptability.
Researchers present a zero-shot LLM system that assesses depression risk from Reddit posts, achieving competitive F1 scores and demonstrating scalable mental-health monitoring.