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

IntroductionPre-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 a...

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Bibliographic Details
Published in:BMJ open 2023-09, Vol.13 (9), p.e074705
Main Authors: Ranjbar, Amene, Taeidi, Elham, Mehrnoush, Vahid, Roozbeh, Nasibeh, Darsareh, Fatemeh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Data collection
Developing countries
Hypertension
LDCs
Machine learning
Medical diagnosis
Medical research
Missing data
Mortality
Obstetrics and Gynaecology
Patients
Preeclampsia
Pregnancy
Systematic review
Womens health
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Summary:IntroductionPre-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 analysisThis 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 learning’. All studies that used machine learning-based analysis for predicting pre-eclampsia in pregnant women will be considered. Non-English articles and those that are unrelated to the topic will be excluded. PROBAST (Prediction model Risk Of Bias ASsessment Tool) will be used to assess the risk of bias and the applicability of each included study.Ethics and disseminationEthical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal.PROSPERO registration numberThis review is registered with PROSPERO (ID: CRD42023432415).
ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2023-074705