本地部署的RAG系统助力医院科研协作推荐
Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments
- 用PubMedBERT+LLaMA3构建本地化检索生成系统
- 基于机构作者论文实现科研合作匹配推荐
- 适合需隐私保护的医院科研场景
大型语言模型（LLMs）在医疗环境中日益成为临床、研究和行政工作的有力工具。然而，医院环境中的严格隐私和网络安全规定要求敏感数据必须在完全本地化的基础设施中处理。在此背景下，我们开发并评估了一个检索增强生成（RAG）系统，用于根据某医疗机构成员撰写的PubMed文献推荐潜在的研究合作者。该系统采用PubMedBERT生成领域特定嵌入，并利用本地部署的LLaMA3模型进行生成式合成。本研究证明了将领域专用编码器与轻量级LLM结合，在本地部署约束下支持生物医学知识发现的可行性与实用性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly recognized as valuable tools across the medical environment, supporting clinical, research, and administrative workflows. However, strict privacy and network security regulations in hospital settings require that sensitive data be processed within fully local infrastructures. Within this context, we developed and evaluated a retrieval-augmented generation (RAG) system designed to recommend research collaborators based on PubMed publications authored by members of a medical institution. The system utilizes PubMedBERT for domain-specific embedding generation and a locally deployed LLaMA3 model for generative synthesis. This study demonstrates the feasibility and utility of integrating domain-specialized encoders with lightweight LLMs to support biomedical knowledge discovery under local deployment constraints.
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