Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models.

ABSTRACT:

Objective

To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively.

Methods

A retrospective, multi-site study of patients opting for expectant or medical management of miscarriage was undertaken. A total of 1075 patients across two hospital early pregnancy units were eligible for inclusion. Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. The area under the receiver operating characteristic curve (AUC) scores were the m

SUBMITTER: Murugesu S 

PROVIDER: S-EPMC11869538 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature