To explore the performance of different mainstream big language models in desensitizing medical documents, a medical desensitization system based on large language models is designed. Mainstream open-source big language models Gemma2, Llama3, Qwen2, and Mistral are adopted as research objects, using the big language model management framework tool Olama for private deployment. By constructing a unified Prompt engineering template as input for the big language model, the ability of the big language model is called in the form of an interface to obtain target sensitive words in medical documents. Sensitive words are replaced to complete the desensitization of medical documents. On the virtual private server, the recognition of sensitive words in a single medical document can be completed within 52.420 to 123.380 seconds. In terms of performance in five types of medical document anonymizations, 12 types of sensitive word recognition, and the processing efficiency of large language models, Gemma2 demonstrates the best overall performance, followed by Llama3, Qwen2, and Mistral. The results show that even without GPU computing power, virtual dedicated servers can still complete sensitive word recognition and processing with high quality by deploying large language models, greatly improving the accuracy of medical document desensitization. 

为了有效地保护患者隐私与数据安全, 探索不同主流大语言模型在医疗文书中脱敏的表现, 设计一套基于大语言模型的医疗脱敏系统。该系统分别以主流开源大语言模型Gemma2, Llama3, Qwen2和Mistral为研究对象, 使用大语言模型管理框架工具 Ollama进行私有化部署; 通过构建统一Prompt提示工程模版作为大语言模型输入, 以接口形式调用大语言模型能力获取医疗文书中目标敏感词, 然后对医疗文书进行敏感词替换, 完成医疗文书的脱敏工作。在虚拟专用服务器上, 单份医疗文书敏感词识别均可在52.420~123.380 s内完成; 在5类医疗文书脱敏、12类敏感词识别以及大语言模型的处理实效性上, Gemma2的整体表现最佳, 其后依次为Llama3, Qwen2和Mistral。结果表明, 在无GPU算力的情况下, 虚拟专用服务器可通过部署大语言模型来高质量地完成敏感词识别和处理, 可以极大地提高医疗文书脱敏的准确性。