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.

翻译：本文探讨了大型语言模型在护理与老年照护领域的应用，重点关注人工智能驱动的患者监测与交互。我们引入了一个新颖的中文护理数据集，并采用增量预训练和监督微调技术，以提升大型语言模型在专业任务中的表现。借助LangChain，我们开发了一个能够提供实时照护与个性化干预的动态护理助手。实验结果显示出显著的性能提升，为通过人工智能驱动的解决方案应对人口老龄化背景下日益增长的医疗保健需求开辟了道路。