Juying Yan

1 Introduction Stroke remains one of the leading causes of mortality and long-term disability worldwide, posing significant challenges to healthcare systems (Katan and Luft 2018). The abrupt onset of neurological deficits in stroke patients necessitates rapid, coordinated and efficient management to minimize brain injury and optimize recovery (Kuriakose and Xiao 2020).

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.