Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework.

ABSTRACT: Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF). SERDIF was evaluated with researchers studying climate-related health hazards of vasculitis disease activity across European countries (N_P1 = 10, N_P2 = 17, N_P3 = 23). Usability metrics consistently improved, indicating SERDIF's effectiveness in

SUBMITTER: Navarro-Gallinad A 

PROVIDER: S-EPMC11452697 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature