Zixuan Song
ORCID: 0009-0001-5128-9874
· 5 papers in corpus
other
2025
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personaliz…
observational
2025
OBJECTIVES: This study aimed to dissect the aetiological subgroups of postpartum haemorrhage (PPH) that occur after vaginal delivery in women with full-term singleton pregnancies. Our goal was to craft and validate predictive models to guid…
meta-analysis
2023
OBJECTIVE: A meta-analysis was conducted to evaluate the overall pregnancy outcomes after uterus-sparing operative treatment in patients with adenomyosis (AD). DATA SOURCES: We searched PubMed, Web of Science, Cochrane Library, and Embase …
observational
2022
BACKGROUND: Postpartum hemorrhage (PPH) is a common complication following vaginal delivery and in severe cases can lead to maternal death. A straightforward predictive model is required to enable prenatal evaluations by obstetricians to pr…
observational
2022
Background: Pernicious placenta previa (PPP) is one of the most dangerous complications in pregnancy after cesarean section, with high perinatal mortality. This study aimed to develop a nomogram to predict postpartum hemorrhage in patients …