EHMQA-GPT: A Knowledge Augmented Large Language Model for Personalized Elderly Health Management
Shaofu Lin, Yidan Duan, Tao Zhou, Xiliang Liu, Jiaojiao Wang · Information · 2025
Due to training limitations, general LLMs often lack sufficient accuracy and practicality in specialized domains such as elderly health management. To help alleviate this issue, this paper introduces EHMQA-GPT, the first domain-specific LLM tailored for non-specialist users (caregivers, elderly individuals, family members, and community health workers) for low-risk, daily health consultations in real-world scenarios. EHMQA-GPT innovates in two aspects: (1) professional corpus construction: we established a multi-dimensional annotation system, integrating EHM-KB, EHM-SFT, and EHM-Eval, to achieve vector representation and hierarchical classification of domain knowledge; and (2) knowledge-enhanced large language model construction: based on ChatGLM3-6B, we integrated knowledge retrieval mechanisms and supervised fine-tuning strategies, enhanced the generation effect through knowledge base retrieval, and achieved deep alignment of domain knowledge through mixed supervised fine-tuning. The experimental verification part adopts testing in six fields. EHMQA-GPT has an accuracy rate of 78.1%, which is 22.3% higher than ChatGLM3-6B. Subjective assessment constructs a dual verification system (GPT-4 automatic scoring + gerontology expert blind review) and is significantly superior to the baseline model in three dimensions: knowledge accuracy (+38.9%), logical coherence (+39.4%), and practical guidance (+31.4%). The proposed framework and corpus provide a novel and scalable foundation for future research and deployment of LLMs in elderly health.