An evidence-weighted knowledge framework with path-aware reasoning for personalized lifestyle management in chronic disease

Abstract

Background Healthy lifestyle management is essential for chronic disease prevention and long-term care. However, evidence linking lifestyle behaviors, biological mechanisms, and health outcomes is dispersed across diverse biomedical sources. This fragmentation makes it difficult for health professionals to efficiently retrieve and interpret evidence needed to inform personalized lifestyle guidance. Methods We constructed an Evidence-Weighted Lifestyle Knowledge Graph (EW-LKG) to organize multidimensional lifestyle evidence by extracting structured triples from over 170,000 PubMed abstracts using schema-based large language model extraction with rule-guided verification. Each relation was assigned a heuristic confidence score based on study design hierarchy and aggregated supporting evidence to facilitate evidence-aware retrieval and reasoning. Path-aware retrieval and multi-hop reasoning were used to identify high-confidence evidence chains that support answer generation by large language models. Performance was evaluated by benchmark question answering across four large language models, external validation on the PubMedQA chronic disease subset, and expert scoring across 5 evaluation dimensions. Results We constructed an evidence-weighted, multidimensional lifestyle knowledge graph integrating diet, physical activity, sleep, stress-related, and social-behavioral factors, comprising 156 k entities and 202 k relations. Path-aware question answering improved accuracy across four large language models, with Qwen3-Max increased by 0.184, Llama-4-Maverick increased by 0.159, DeepSeek V3.2-Exp increased by 0.161 and GPT-5.2 increased by 0.159. Correct answers were also associated with higher PathScores (0.80 vs. 0.62), particularly for mechanism-mediated and multi-factor reasoning. Results showed insensitivity across alternative evidence weighting schemes, and source-disjoint validation on the lifestyle-domain benchmark suggested that gains derived from structured knowledge integration rather than corpus overlap; on the general-domain PQA-L split, gains were smaller and narrowed in the source-disjoint subset (0.598 vs. 0.528; 0.464 vs. 0.435). External validation on PubMedQA chronic disease questions yielded consistent performance. Expert evaluations across five dimensions showed an average gain of 4.34 points. Conclusion The EW-LKG provides a structured approach for organizing literature-derived lifestyle evidence and selecting evidence-linked reasoning paths. In the evaluated datasets, path-aware retrieval improved QA accuracy across multiple LLMs. As an evidence-organizing framework, it may support more systematic and evidence-informed lifestyle management, while further validation in real-world clinical and public health settings is needed.