Development and Validation of An Interpretable Machine Learning-Based Prediction Model of Postpartum Hemorrhage in Placenta Previa Following Cesarean Section: A Multicenter Study.

ABSTRACT: The objective of this study is to predict the occurrence of postpartum hemorrhage in women with placenta previa based on machine learning. This retrospective study enrolled 845 singleton pregnant patients with placenta previa from two hospitals. They were allocated into a training cohort (n = 403), a testing cohort (n = 174), and the external validation cohort (n = 268). Univariate and multivariate regression analyses were employed to select clinical variables (p < 0.05), which were subsequently utilized to develop 11 machine learning prediction models. The area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used to evaluate the performance of the models. Besides, SHapley Additive exPlana

SUBMITTER: Li M 

PROVIDER: S-EPMC12443912 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature