Application of knowledge graphs in health management for chronic disease patients: a scoping review

Chronic diseases impose a significant burden on global health. Knowledge graphs (KGs), which integrate and represent multisource information as interconnected networks, provide a promising approach for enabling personalized and dynamic health management. The aim of this scoping review is to systematically map the current landscape of KG applications in chronic disease health management.

In accordance with the Arksey and O'Malley framework and the PRISMA-ScR guidelines, a systematic search was conducted in eight databases, namely, the Wanfang Database, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, and CINAHL, from the establishment of the databases to August 2025. Two researchers independently screened the literature and extracted data on the basis of the inclusion and exclusion criteria.

A total of 15 studies published between 2018 and 2025 were included. In these studies, the KG construction process generally involved five stages: data collection, information extraction, knowledge fusion, graph construction, and visualization. KGs were applied across a range of chronic diseases, including metabolic, cardiovascular, and respiratory diseases, as well as cancers. In terms of application scenarios, self-health management was the most common (9 studies, 60.0%), followed by clinical decision support (5 studies, 33.3%), intelligent question answering (4 studies, 26.7%), and health monitoring/early warning (1 study, 6.7%). With respect to system maturity, 12 studies (80.0%) were classified as prototype systems, 3 studies (20.0%) were classified as pilot implementations, and none were classified as clinically deployed systems. The reported outcomes were mainly technical or feasibility oriented, and 7 studies (46.7%) did not report any explicit evaluation metrics.

Knowledge graphs show promise for integrating heterogeneous health data in chronic disease management. However, research has focused predominantly on system development and proof-of-concept validation, with limited high-quality evidence regarding clinical effectiveness. To support clinically meaningful implementation, in future work, rigorous real-world evaluation should be prioritized and challenges related to data quality, interoperability, and model interpretability should be addressed.