使用知识图表和大型语言模型构建智能糖尿病问答系统
Zhenkai Qin1,2, Dongze Wu1, Zhidong Zang3
1School of Information Technology, Guangxi Police College, Nanning, China.
Frontiers in public health
|March 7, 2025
概括
这项研究开发了一个使用大型语言模型和知识图表的智能系统,为糖尿病患者提供个性化的医疗信息. 该系统提高了医疗指导的准确性和相关性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 传统的医疗保健系统在糖尿病管理方面难以处理复杂的医疗问题.
- 个性化的医疗信息传递对于有效的糖尿病护理至关重要.
- 现有的问答系统缺乏对细微的医疗指导的上下文理解.
研究的目的:
- 引入智能问答系统,为糖尿病患者提供个性化的医疗信息.
- 提高糖尿病管理医疗指南的准确性和上下文相关性.
- 解决当前医疗保健系统在处理复杂患者查询方面的局限性.
主要方法:
- 整合大型语言模型 (Baichuan2-13B,Qwen2.5-7B) 与基于Neo4j的知识图.
- 应用低级适应 (LoRA) 和基于提示的学习技术,以改善语义理解和响应生成.
- 通过实体识别和意图分类任务评估系统性能.
主要成果:
- 在实体识别方面达到85.91%的精度,在意图分类方面达到88.55%的精度.
- 通过知识图集成,在准确性和临床相关性方面显著改善.
- 展示了为糖尿病管理提供个性化医疗响应的增强能力.
结论:
- 大型语言模型和知识图的整合有效地改善了医疗问答系统.
- 拟议的方法为通过个性化医疗保健推进糖尿病管理提供了一个有希望的框架.
- 这种方法为未来在各种医疗保健应用中进行个性化干预提供了基础.
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