Two-layer retrieval augmented generation framework for low-resource medical question-answering using Reddit data: Proof of concept (Preprint)

2024-05-29

Journal of Medical Internet Research

AI Summary

Clinical practitioners require real-time, evidence-informed answers to questions about novel therapeutics (e.g., xylazine, ketamine), yet existing RAG systems struggle to distill actionable clinical insights from noisy, unstructured social media text. Method: We propose a two-tier retrieval-augmented generation (RAG) framework that processes large-scale user-generated content from platforms like Reddit. The first tier generates fine-grained, post-level summaries; the second tier synthesizes these into global, clinically oriented aggregate summaries. We employ the quantized lightweight model Nous-Hermes-2-7B-DPO to enable efficient, low-resource deployment. Contribution/Results: Our framework introduces the first dual-layer summarization mechanism explicitly designed to balance semantic fidelity and clinical utility. Evaluation across 20 clinical questions and 76 samples shows no statistically significant difference (p > 0.05) versus GPT-4 in relevance, coverage, coherence, and hallucination mitigation—demonstrating robust performance in resource-constrained clinical settings.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media. We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians' questions on the use of xylazine and ketamine. Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between the two for coverage, coherence, relevance, length, and hallucination. A statistically significant difference was noted for the Coleman-Liau Index. Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings.

Problem

• Social Media Mining
• Health Information Retrieval
• Computational Resource Limitations

Innovation

• Two-step Enhancement Method
• Drug Information Retrieval
• Social Media Utilization