Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China.

ABSTRACT:

Background

Postpartum haemorrhage is the most common complication associated with vaginal birth and a principal cause of maternal mortality. While clinical guidelines suggest that the majority of postpartum haemorrhage cases can be averted through precise prediction and scientific management that utilise assessment tools, existing tools for predicting postpartum haemorrhage in vaginal births have demonstrated inadequacies.

Aim

To develop a predictive model for postpartum haemorrhage in vaginal births based on machine-learning algorithms.

Methods

We selected pregnant women who gave birth vaginally at a tertiary-level obstetrics and gynaecology hospital in Shanghai, China, from July 2023 to August 2024. Multidimensional data were collected on demographic factors of preg

SUBMITTER: Yao X 

PROVIDER: S-EPMC12570276 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature