Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework

TL;DR

This paper presents an AI-driven framework utilizing large language models to improve nursing and elderly care through specialized datasets, incremental training, and real-time patient interaction capabilities.

Contribution

It introduces a novel Chinese nursing dataset and combines IPT and SFT techniques to enhance LLM performance in healthcare tasks.

Findings

Significant performance improvements in nursing tasks

Development of a real-time nursing assistant using LangChain

Potential for scalable AI-driven elderly care solutions

Abstract

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.