Labour monitoring and decision support: a machine-learning-based paradigm.

ABSTRACT:

Introduction

A machine-learning-based paradigm, combining unsupervised and supervised components, is proposed for the problem of real-time monitoring and decision support during labour, addressing the limitations of current state-of-the-art approaches, such as the partograph or purely supervised models.

Methods

The proposed approach is illustrated with World Health Organisation's Better Outcomes in Labour Difficulty (BOLD) prospective cohort study data, including 9,995 women admitted for labour in 2014-2015 in thirteen major regional health care facilities across Nigeria and Uganda. Unsupervised dimensionality reduction is used to map complex labour data to a visually intuitive space. In this space, an ongoing labour trajectory can be compared to those of a historical cohort

SUBMITTER: Nogueira M 

PROVIDER: S-EPMC12040997 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature