用大模型提升养老护理，实现智能监测与个性化干预
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework
- 构建中文护理数据集，通过增量预训练与微调提升模型能力
- 实测显示系统在实时护理和个性化干预中性能显著提升
- 适合关注智慧养老与医疗AI落地的研究者与从业者
本文探讨了大型语言模型（LLMs）在护理与老年照护中的应用，聚焦于AI驱动的患者监测与交互。我们引入了一个新的中文护理数据集，并采用增量预训练（IPT）与监督微调（SFT）技术，提升模型在专业任务中的表现。基于LangChain，开发了一个可动态响应的护理助手，支持实时照护与个性化干预。实验结果表明性能显著提升，为应对老龄化社会日益增长的医疗需求提供了可行的AI解决方案。
原文摘要 · Abstract (English)
This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.
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