A retrieval-augmented model guided zero-shot prompt for LLM to detect mental disorders in social media text

An ontology-guided knowledge graph and retrieval-augmented LLM framework for interpretable mental health assessment

An ontology-guided framework that integrates a Knowledge Graph, an Ontology-Informed Retrieval Classifier, and a Large Language Model for interpretable mental health detection from social media text demonstrates that the KG–ORC cross-validation gate measurably improves predictive reliability over single component basel...

BUAP-NLP Lab at MentalRiskES 2026: Zero-Shot Prompting with Heterogeneous Large Language Model Selection for Early Detection of Mental Health Symptoms in Therapeutic Conversations

Domain-informed density extraction for robust mental health classification of long social media posts

The findings demonstrate that a leakage-safe evaluation protocol is essential for producing credible results in mental health classification from long social media posts and show that domain-informed density extraction provides a robust and practical text representation, with its greatest benefits emerging when models...

Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study

Synergizing a curated domain-specific knowledge base with LLMs via a RAG architecture is an effective strategy for accurately identifying ADEs in unstructured Chinese clinical notes, providing a foundational open-source benchmark and a robust technical framework to advance pharmacovigilance, drug safety research, and c...

Multi-Class Mental Health Discourse Classification from Online Texts: Classical ML vs Transformer/LLM Modeling

It is shown that con-textual transformer/LLM models yield more reliable macro-level performance under imbalance than TF–IDF baselines, particularly for semantically adja- cent classes.

Incremental Context Expansion and Domain-Adapted Transformers for Early Mental Health Risk Detection in Spanish

Early detection of mental health disorders in Spanish is limited by two main factors: the scarcity of domain-specific Spanish language models and the difficulty of making timely predictions from long, progressively available social media histories. This paper addresses these challenges through four main contributions....