精细调整大型语言模型,以在住宅老年护理中提供有效的营养支持:一个领域专业知识方法
Mohammad Alkhalaf1, Dinithi Vithanage2, Jun Shen2
1School of Computer Science, Qassim University, Qassim 51452, Saudi Arabia.
Healthcare (Basel, Switzerland)
|October 29, 2025
概括
一个专门的语言模型使用护理笔记准确地识别住院养老院 (RAC) 居民的营养不良. 这种方法可以提高对面临营养不良风险的老年人的早期发现和干预.
科学领域:
- 医疗保健中的人工智能
- 临床数据的自然语言处理.
背景情况:
- 营养不良是老年人居住老年护理 (RAC) 中的重要健康问题.
- 早期识别营养不良对于及时有效的干预至关重要.
- 基于变压器的大型语言模型 (LLM) 为临床任务提供了先进的功能.
研究的目的:
- 为RAC设置开发一个特定领域的LLM.
- 利用LLM来改善老年人营养不良的识别和预测.
- 加强人工智能在医疗保健中的应用,用于老年护理.
主要方法:
- 一个基于RoBERTa的LLM在RAC电子健康记录 (EHR) 中的50万份护理进度笔记上进行了微调.
- 该模型的嵌入式被用于营养不良说明识别和预测任务.
- 对各种已建立的NLP模型进行了性能评估,使用5倍交叉验证.
主要成果:
- 该RAC领域特定的LLM在营养不良说明识别方面获得了0.966的高F1得分.
- 该模型在营养不良预测方面表现强,F1得分为0.687.
- 开发的LLM超越了基线和其他基于BERT的模型.
结论:
- 为RAC设置开发专门的LLM可用于识别和预测营养不良.
- 这种人工智能驱动的方法支持针对老年人营养不良的早期干预策略.
- 未来的研究将专注于优化预测准确性和将模型集成到临床工作流程中.
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