NDLI: Machine learning models for predicting preeclampsia: a systematic review

Content Provider	Springer Nature : BioMed Central
Author	Ranjbar, Amene Montazeri, Farideh Ghamsari, Sepideh Rezaei Mehrnoush, Vahid Roozbeh, Nasibeh Darsareh, Fatemeh
Abstract	Background This systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia. Method This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023. Search terms were limited to “preeclampsia” AND “artificial intelligence” OR “machine learning” OR “deep learning.” All studies that used ML-based analysis for predicting preeclampsia in pregnant women were considered. Non-English articles and those that are unrelated to the topic were excluded. The PROBAST was used to assess the risk of bias and applicability of each included study. Results The search strategy yielded 128 citations; after duplicates were removed and title and abstract screening was completed, 18 full-text articles were evaluated for eligibility. Four studies were included in this review. Two studies were at low risk of bias, and two had low to moderate risk. All of the study designs included were retrospective cohort studies. Nine distinct models were chosen as ML models from the four studies. Maternal characteristics, medical history, medication intake, obstetrical history, and laboratory and ultrasound findings obtained during prenatal care visits were candidate predictors to train the ML model. Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random forest were among the best models to predict preeclampsia. All four studies used metrics such as the area under the curve, true positive rate, negative positive rate, accuracy, precision, recall, and F1 score. The AUC of ML models varied from 0.860 to 0.973 in four studies. Conclusion The results of studies yielded high prediction performance of ML models for preeclampsia risk from routine early pregnancy information.
Related Links	https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-023-06220-1.pdf
Ending Page	6
Page Count	6
Starting Page	1
File Format	HTM / HTML
ISSN	14712393
DOI	10.1186/s12884-023-06220-1
Journal	BMC Pregnancy and Childbirth
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-01-02
Access Restriction	Open
Subject Keyword	Reproductive Medicine Maternal and Child Health Gynecology Artificial intelligence Machine learning Preeclampsia Systematic review
Content Type	Text
Resource Type	Article
Subject	Obstetrics and Gynecology
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3.4/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach

Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach

Predicting mother and newborn skin-to-skin contact using a machine learning approach

Glycosylated fibronectin in preeclampsia: a systematic review and meta-analysis

Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach

Assisted reproductive technology and the risk of preeclampsia: an updated systematic review and meta-analysis

Evaluating maternal serum sortilin levels: a potential biomarker for predicting preeclampsia

Predicting maternal risk level using machine learning models

Potential biological therapies for severe preeclampsia: a systematic review and meta-analysis

Machine learning models for predicting preeclampsia: a systematic review

Similar Documents

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach

Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach

Predicting mother and newborn skin-to-skin contact using a machine learning approach

Glycosylated fibronectin in preeclampsia: a systematic review and meta-analysis

Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach

Assisted reproductive technology and the risk of preeclampsia: an updated systematic review and meta-analysis

Evaluating maternal serum sortilin levels: a potential biomarker for predicting preeclampsia

Predicting maternal risk level using machine learning models

Potential biological therapies for severe preeclampsia: a systematic review and meta-analysis

Machine learning models for predicting preeclampsia: a systematic review