Leveraging Artificial Intelligence for Enhanced Platelet Management in Dengue Fever

Dengue fever poses a considerable therapeutic challenge owing to its erratic course and the potential for severe thrombocytopenia necessitating prompt care. Utilizing artificial intelligence (AI) provides a revolutionary method for platelet management by facilitating early forecasting of the platelet's lowest point, recovery trajectories, and transfusion requirements. This study delineates a comprehensive AI-driven architecture that incorporates clinical characteristics such as age, fever duration, haematocrit levels, and white blood cell trends into supervised learning models, including Random Forest, XGBoost, and LSTM. These models, incorporated into real-time clinical decision support tools, are intended for seamless integration with hospital information systems and deliver actionable alarms for significant platelet reductions. The methodology prioritizes transparency, data confidentiality, and fair model efficacy across diverse demographics. This AI-enabled solution, through prospective validation in dengue-endemic locations and synchronization with national health systems, aims to enhance transfusion procedures, alleviate hospital strain, and markedly improve patient outcomes during dengue outbreaks.