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.