个性化糖尿病治疗支持使用大型语言模型对电子健康记录进行微调:开发和评估研究研究
Shenyang He1, Yu Zhang2, Jiaxi Li1
1Department of Clinical Laboratory Medicine, Jinniu Maternity and Child Health Hospital of Chengdu, Chengdu, 610041, China, 86 18227387870.
JMIR formative research
|February 9, 2026
概括
这项研究开发了一个大型语言模型 (LLM) 系统,用于个性化糖尿病治疗建议. 微调的GLM4-9B模型在改善糖尿病管理的临床决策支持方面显示出前景.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 糖尿病管理 糖尿病管理
背景情况:
- 个性化治疗对于有效的糖尿病管理至关重要.
- 人工智能 (AI),特别是大型语言模型 (LLM),为增强临床决策支持提供了新的途径.
- LLM可以生成个性化的患者建议.
研究的目的:
- 开发和评估基于LLM的糖尿病门诊治疗支持系统.
- 评估系统在常规临床决策中的潜在价值.
主要方法:
- 三个紧的LLM (Llama 3.1-8B,Qwen3-8B,GLM4-9B) 在无人身份的电子健康记录上使用参数有效的低级调整进行了微调.
- 一个检索增强生成框架将优化模型嵌入到原型医院信息系统中.
- 该系统生成了个性化的治疗建议,实验室测试建议和药物提示.
主要成果:
- 微调的GLM4-9B模型在评估的LLMs中表现出卓越的表现.
- 它产生了临床上合理的治疗计划,实验室测试建议和药物建议.
- 定量评估指标包括双语评估研究对4克 (67.93) 和回忆导向的研究对4克 (44.30),大图 (27.34) 和最长常见次序 (37.67) 的评价得分.
结论:
- 微调的GLM4-9B显示出作为个性化糖尿病护理的临床决策支持工具的巨大潜力.
- 该系统可以提供参考建议,以提高临床医生的效率和决策质量.
- 未来的研究应该专注于改善药物指导,结合多样化的数据来源,并适应复杂的并发症.
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