Decision-making in pediatric blunt solid organ injury: a deep learning approach to predict massive transfusion, need for operative management, and mortality risk

Machine learning models using clinical, laboratory, and imaging data from the first 4 hours can predict which pediatric blunt solid organ injury patients will require massive transfusion, operative intervention, or face mortality risk with over 90% accuracy. This approach may help clinicians identify high-risk patients earlier than traditional vital signs and hemoglobin monitoring alone.