Adaptive Reinforcement Learning-Based Personalized Treatment for Sickle Cell Disease

This Sickle Cell Disease (SCD) is a genetic disorder characterized by the deformation of red blood cells, leading to severe health complications.Existing treatment modalities are often generalized and fail to address individual patient needs.This study proposes an Adaptive Reinforcement Learning (ARL) framework to develop personalized treatment plans for SCD patients.The ARL model integrates patient-specificdataandrealtimefeedbacktooptimizetherapeuticinterventionsdynamically.Enhancedfeatures,such as predictive complication alerts, multi-agent collaboration, real-time genomic updates, and explainable AI mechanisms, are introduced to address limitations in traditional care.By leveraging these advancements, this study aims to improve patient outcomes and demonstrate ARL's transformative potential in personalized medicine.