Search

Chronic kidney disease (CKD) affects 10–15% of adults worldwide and often progresses silently toward kidney failure requiring dialysis or transplantation. Monitoring longitudinal markers such as estimated glomerular filtration rate, creatinine, and albuminuria is essential for early intervention and delaying disease progression. However, current predictive models typically rely on static or isolated clinical features, limiting their ability to capture dynamic interactions between laboratory trends, medications, and comorbidities, which leads to incomplete risk assessment. To address this limitation, a conceptual framework based on a graph convolutional network with attention mechanisms is proposed to integrate longitudinal laboratory data, medication networks, and comorbidity structures for CKD progression prediction. Patient records from 2017–2023 are represented as a heterogeneous graph, where nodes include laboratory values, drugs, and diagnoses, and edges encode clinical and pharmacological relationships. Graph convolutional layers capture relational patterns, while attention mechanisms highlight the most clinically relevant interactions, enabling more informative patient-level representations for risk prediction across CKD stages. This approach improves interpretability by revealing which laboratory trends, medications, and comorbidities most influence predicted outcomes, aligning model behavior with clinical nephrology knowledge. Overall, the framework provides a unified and scalable strategy for more accurate and interpretable CKD progression risk prediction by leveraging relational and temporal data structures that traditional models fail to exploit.