CounselingQA: scaling professional mental-health guidance via dense retrieval and large language models

A Web-Based Mental Health Assessment Framework using Natural Language Processing Techniques

Mental illnesses like depression, anxiety, stress, and so on have become more widespread, and this has necessitated the availability of assessment tools that are readily available, scalable, and automated. The paper will offer a web-based mental health risk assessment system that utilizes the latest technology of Natural Language Processing (NLP) in real-time to analyze textual information provided by users. The offered system incorporates a hybrid deep learning framework with the relation of DeBERTa, BiLSTM, and XGBoost to promote the contextual comprehension, sequential emotional pattern identification, and effective classification of the performance. First, text input by the users is received with a secure web interface and processed with general NLP preprocessing methods, such as tokenization, lemmatization, and sentiment normalization. To extract deep semantic relationships in the text, DeBERTa is used to extract contextual embeddings. Such embeddings are also trained in the form of a Bidirectional Long Short-Term Memory (BiLSTM) network in order to capture emotional dynamics and linguistic reinforcing relations. Fused feature representation, sentiments, and linguistic indicators are input into an XGBoost classifier to predict mental health in multi-class. There is a weighted risk scoring system used to measure the level of severity and provide tailored feedback. It is experimentally tested on standard mental health text data sets that the proposed hybrid framework is more effective than the traditional machine learning and standalone transformer models in terms of accuracy, precision, recall, and F1-score. The architecture is scalable to the deployment of a web system, which is guaranteed to perform in real-time, secure data, and privacy of users. The suggested framework offers a solid and smart instrument to identify the risk of mental health early and help intervene in time and to promote the development of digital health care.

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

Mental health detection from social media has gained increasing attention due to the spontaneous expression of psychological states on online platforms. However, accurate interpretation remains challenging because social media language is highly contextual, ambiguous, and often includes sarcasm, metaphors, or non-clinical expressions, which can lead to unreliable predictions. Existing approaches based on machine learning, deep learning, transformers, and large language models (LLMs) primarily rely on text-driven patterns and often lack explicit ontology-guided grounding, structured knowledge validation, and interpretability. Although LLMs improve contextual reasoning, their predictions can be inconsistent and prone to hallucination when not supported by structured domain knowledge and a unified cross-component validation mechanism. To address these limitations, this paper proposes an ontology-guided framework that integrates a Knowledge Graph (KG), an Ontology-Informed Retrieval Classifier (ORC), and a Large Language Model for interpretable mental health detection from social media text. The framework leverages the Human Phenotype Ontology (HPO) to guide symptom extraction and Knowledge Graph construction, normalizing informal linguistic expressions into ontology-grounded representations. A key design contribution is an explicit KG–ORC cross-validation consistency gate, which requires both the Knowledge Graph and the ORC module to independently reach agreement before a prediction is committed reducing erroneous inferences from either component alone. The LLM is deliberately restricted to a post-validation role, generating human-readable explanations and supportive recommendations only from validated outputs, thereby reducing hallucination risks and preserving knowledge-consistency. The proposed framework is evaluated on the Dreaddit dataset, a large-scale, multi-domain Reddit corpus for stress and mental health analysis. Empirical evaluation on the HPO guided symptom extraction confirms differential detection rates of 56.04% in stress positive posts versus 31.11% in stress negative posts, validating the signal relevance of the ontology-driven extraction. End-to-end binary classification on the held-out test split achieves an accuracy of 74.82% with an F1-score of 75.64%. Crucially, on the 66.55% of instances where the KG and ORC modules reach consensus, the consistent subset accuracy rises to 78.84% (F1 = 78.28%), compared to 63.03% for the Knowledge Graph alone. The KG–ORC agreement rate across the full test set is 82.02%. These results demonstrate that the KG–ORC cross-validation gate measurably improves predictive reliability over single component baselines, and that ontology-guided, knowledge-grounded reasoning offers a principled path toward interpretable and knowledge-consistent mental health analysis from social media.

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

This work extends PubHealthBench, a question answering benchmark of 7,929 questions derived from UK Government public health guidance, into a retrieval-augmented setting and systematically evaluates retrieval and generation choices, and introduces a rubric-based LLM-as-a-judge covering faithfulness, completeness, clarity, and factual consistency.

JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

Comparing Human and Large Language Model Responses to Patients Online Questions: Towards Multi-dimensional Patient-centered Support

Patients and caregivers seek informational and emotional support throughout medical care, especially when interpreting unfamiliar laboratory test results. Although resources such as patient portals and online health communities (OHCs) help address questions, gaps remain. The emergence of large language models (LLMs) offers the potential to be a complementary source of support to assist patients and caregivers in understanding and using their test results. The objective of our study is to empirically compare LLM responses to patients online questions containing their laboratory test results to responses written by peers in an OHC. We compared the 519 peer replies to 122 laboratory test-related posts from an OHC to 488 responses generated from four LLMs using mixed computational and qualitative methods. LLMs frequently provided clear explanations of medical terminology and structured interpretations of numeric results but were longer and less readable. Peers offered more personalized, context-specific emotional support. Overall, LLMs have the potential to complement peer responses in OHCs, but require greater emotional depth, reasoning transparency, and alignment with community norms.

Healthmate: An AI Chatbot for Symptom-Based Healthcare Consultation

HealthMate is presented, an intelligent, explainable AI chatbot framework designed for preliminary healthcare consultation that demonstrates rapid retrieval, robust natural language comprehension, and clear explainability without replacing professional medical diagnosis.