为南非结核病诊疗定制大模型，提升医疗问答准确性。
Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa
- 基于南非结核病指南与医学数据，用QLoRA微调模型并引入GraphRAG增强检索。
- 在语义、知识和上下文匹配上优于基础模型和通用大模型。
- 适合医疗AI研究者与基层结核病诊疗辅助系统开发者参考。
结核病是全球最致命的传染病之一，在南非其对医疗体系造成沉重负担。本文开展实验研究，开发针对南非结核病护理领域的专用大语言模型（DS-LLM），以减轻患者和医护人员压力。研究首先通过文献综述梳理医疗领域大模型开发策略，随后收集南非结核病指南、相关文献及现有基准医学数据集。采用量化低秩适配（QLoRA）算法对医学大模型BioMistral-7B进行微调，并结合图谱增强生成（GraphRAG）技术。所构建的DS-LLM通过自动化指标与量化评分，与基础模型BioMistral-7B及通用大模型对比评估。结果显示，该模型在南非结核病护理场景下的上下文一致性（词汇、语义与知识层面）显著优于基线模型。
原文摘要 · Abstract (English)
Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health care system. This paper presents an experimental study on the development of a domain-specific Large Language Model (DS-LLM) for TB care that can help to alleviate the burden on patients and healthcare providers. To achieve this, a literature review was conducted to understand current LLM development strategies, specifically in the medical domain. Thereafter, data were collected from South African TB guidelines, selected TB literature, and existing benchmark medical datasets. We performed LLM fine-tuning by using the Quantised Low-Rank Adaptation (QLoRA) algorithm on a medical LLM (BioMistral-7B), and also implemented Retrieval-Augmented Generation using GraphRAG. The developed DS-LLM was evaluated against the base BioMistral-7B model and a general-purpose LLM using a mix of automated metrics and quantitative ratings. The results show that the DS-LLM had better performance compared to the base model in terms of its contextual alignment (lexical, semantic, and knowledge) for TB care in South Africa.
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