Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework
Abstract
Large language models enhanced with incremental pre-training and supervised fine-tuning are applied to develop a dynamic nursing assistant using a new Chinese nursing dataset, showing improved performance in patient care and interaction.
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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