Chronic Kidney Disease (CKD) affects approximately 10% of the global population, with significant underdetection, particularly among vulnerable populations. These groups face greater disparities in CKD incidence and progression due to poverty, limited healthcare access, and environmental risks. Yet, these same vulnerabilities often prevent them from being screened, compounding health inequities. By leveraging AI to create a data-driven CKD Population Vulnerability Index and an interactive dashboard, our solution empowers researchers and public health officials to pinpoint geographic areas where targeted screenings would bring the greatest impact, enabling more equitable and cost-effective interventions.

Our system integrates population-level and individual-level predictive models, designed to identify the key socio-demographic and environmental factors driving CKD risk. The population model maps these predictive factors at the community level, while the individual model assesses patient-specific risk and disease progression. These insights are synthesized into the AI-driven CKD Population Vulnerability Index, which is visualized through an interactive dashboard.

This project was prepared as a capstone project for the AI in Health Care: From Strategies to Implementation program at Harvard Medical School Executive Education. It is being developed in collaboration with researchers and professionals from Clínica de la Costa and Universidad Simón Bolívar in Barranquilla, as well as Universidad del Salvador in Argentina.