Artificial intelligence platform to predict children's hospital care for respiratory disease using clinical, pollution, and climatic factors

We developed a platform that integrates clinical and environmental databases within a big data framework to process and analyse information using AI techniques. This tool predicts daily emergency department and hospital admission flows related to paediatric respiratory diseases. The algorithms can distinguish whether a child arriving at the emergency department is likely to be treated and discharged or will require hospital admission. This predictive capability may support hospital planning and resource allocation, particularly in contexts of environmental vulnerability.