Identifying risk of stillbirth using machine learning.

ABSTRACT:

Background

Previous predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling nonlinear relationships between outcomes.

Objective

This study aimed to create and refine machine learning models for predicting stillbirth using data available before viability (22-24 weeks) and throughout pregnancy, as well as demographic, medical, and prenatal visit data, including ultrasound and fetal genetics.

Study design

This is a secondary analysis of the Stillbirth Collaborative Research Network, which included data from pregnancies resulting in stillborn and live-born infants delivered at 59 hospitals in 5 diverse regions across the United States from 2006 to 20

SUBMITTER: Cersonsky TEK 

PROVIDER: S-EPMC10527568 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature