@inproceedings{8e963a60519e4027823bfbe79ddb644b,
title = "Retrieval Augmented Generation System for Mental Health Information",
abstract = "Retrieval Augmented Generation (RAG) systems enable LLMs to avoid hallucinations and thus can be used for knowledge-intensive tasks that require higher accuracy. RAG systems have been developed for a variety of purposes, including some health-related domains. But developing a RAG system specifically tailored for mental health and based on highly reputable scientific evidence has not been considered. This paper proposes an accurate RAG system for mental health. The proposed RAG system can be useful for policymakers in designing specific interventions for mental health issues by building further support systems around it. The RAG system can be extended through more knowledge base and can help in mental health counselling. This study validates the utility of RAG systems in augmenting information retrieval for mental health, emphasizing the importance of leveraging external knowledge bases to ensure data accuracy and reliability.",
keywords = "Retrieval augmented generation, information retrieval, large language models, mental health, natural language processing",
author = "Shah, \{Hurmat Ali\} and Ashhadul Islam and Tariq, \{Zain Ul Abideen\} and Belhaouari, \{Samir B.\} and Mowafa Househ",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 20th World Congress on Medical and Health Informatics, MEDINFO 2025 ; Conference date: 09-08-2025 Through 13-08-2025",
year = "2025",
month = aug,
day = "7",
doi = "10.3233/SHTI250929",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "693--697",
editor = "Househ, \{Mowafa S.\} and Househ, \{Mowafa S.\} and Tariq, \{Zain Ul Abideen\} and Mahmood Al-Zubaidi and Uzair Shah and Elaine Huesing",
booktitle = "MEDINFO 2025 - Healthcare Smart x Medicine Deep",
address = "Netherlands",
}