Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review.

ABSTRACT:

Background

Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).

Objective

This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.

Methods

This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search

SUBMITTER: Sharifi-Heris Z 

PROVIDER: S-EPMC9069277 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature