Machine learning models for predicting pre-eclampsia: a systematic review protocol.

ABSTRACT:

Introduction

Pre-eclampsia is one of the most serious clinical problems of pregnancy that contribute significantly to maternal mortality worldwide. This systematic review aims to identify and summarise the predictive factors of pre-eclampsia using machine learning models and evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia.

Methods and analysis

This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This search strategy includes the search for published studies from inception to January 2023. Databases include the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus and Google Scholar. Search terms include 'preeclampsia' AND 'artificial intelligence' OR 'machine learning' OR 'deep

SUBMITTER: Ranjbar A 

PROVIDER: S-EPMC10496701 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature