The application relates to a chronic
kidney disease prediction method based on
clinical information graph representation, belongs to the technical field of computer-
aided diagnosis of chronic
kidney disease (CKD), and aims to solve the problem of
missed diagnosis caused by the fact that the
kidney function index of early CKD is not obvious. The incidence of CKD is high, the early symptoms are hidden, and the
disease is easy to be missed, thus developing into end-stage renal disease. Therefore, the application provides a chronic
kidney disease prediction method based on
clinical information graph representation. The method first extracts
fundus image and clinical index features; the clinical index is fused into a clinical index joint feature through text embedding and numerical
feature fusion, and the joint feature is fused with the
fundus image feature through cross-
modal attention; the similarity between subjects is calculated based on the fused feature, and a subject relationship graph is constructed by using an adaptive dynamic threshold mechanism; finally, a
hybrid graph neural network is used for graph representation learning,
pathological similarity between subjects is mined, and accurate prediction of chronic
kidney disease is realized. The application can improve the
detection rate of early CKD and is applied to non-invasive early screening and risk early warning.