Donglai Hu

Abstract Accurate identification of early pediatric abdominal sepsis (PAS) is essential to improving outcomes, yet most existing pediatric sepsis criteria and scoring tools primarily focus on cardiopulmonary dysfunction and overlook early intra-abdominal infections. To address this gap, we combined the real-world data with explainable machine learning to develop the Abdominal Sepsis Diagnosis model (ABSeD) for clinical decision support.