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

Qiao Sun, Jiexin Xie, Nanyang Ye, Qinying Gu, Shijie Guo


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 an interactable 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.
Anthology ID:
2025.coling-main.673
Volume:
Proceedings of the 31st International Conference on Computational Linguistics
Month:
January
Year:
2025
Address:
Abu Dhabi, UAE
Editors:
Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10083–10090
Language:
URL:
https://aclanthology.org/2025.coling-main.673/
DOI:
Bibkey:
Cite (ACL):
Qiao Sun, Jiexin Xie, Nanyang Ye, Qinying Gu, and Shijie Guo. 2025. Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10083–10090, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework (Sun et al., COLING 2025)
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PDF:
https://aclanthology.org/2025.coling-main.673.pdf