{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T23:40:30Z","timestamp":1793749230338,"version":"4.6.1"},"reference-count":12,"publisher":"BMJ","issue":"9","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"unspecified","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2023,8,22]]},"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>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 \u2018preeclampsia\u2019 AND \u2018artificial intelligence\u2019 OR \u2018machine learning\u2019 OR \u2018deep learning\u2019. 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>Ethical 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>PROSPERO registration number<\/jats:title>\n                    <jats:p>This review is registered with PROSPERO (ID: CRD42023432415).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2023-074705","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T13:40:42Z","timestamp":1694439642000},"page":"e074705","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":32,"title":["Machine learning models for predicting pre-eclampsia: a systematic review protocol"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2390-702X","authenticated-orcid":false,"given":"Amene","family":"Ranjbar","sequence":"first","affiliation":[{"name":"Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Taeidi","sequence":"additional","affiliation":[{"name":"Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vahid","family":"Mehrnoush","sequence":"additional","affiliation":[{"name":"Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasibeh","family":"Roozbeh","sequence":"additional","affiliation":[{"name":"Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2644-792X","authenticated-orcid":false,"given":"Fatemeh","family":"Darsareh","sequence":"additional","affiliation":[{"name":"Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"2025121222470674000_13.9.e074705.1","doi-asserted-by":"publisher","DOI":"10.1097\/01.AOG.0000437382.03963.88"},{"key":"2025121222470674000_13.9.e074705.2","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000003891"},{"key":"2025121222470674000_13.9.e074705.3","doi-asserted-by":"crossref","DOI":"10.1155\/2011\/481095","article-title":"Public health perspectives of preeclampsia in developing countries: implication for health system strengthening","volume":"2011","author":"Osungbade","year":"2011","journal-title":"J Pregnancy"},{"key":"2025121222470674000_13.9.e074705.4","unstructured":"Maternal mortality. n.d. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/maternal-mortality"},{"key":"2025121222470674000_13.9.e074705.5","doi-asserted-by":"crossref","first-page":"100100","DOI":"10.1016\/j.ajogmf.2020.100100","article-title":"Early prediction of preeclampsia via machine learning","volume":"2","author":"Mari\u0107","year":"2020","journal-title":"Am J Obstet Gynecol MFM"},{"key":"2025121222470674000_13.9.e074705.6","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2022-067661","article-title":"Machine learning approach to predict postpartum haemorrhage: a systematic review protocol","volume":"13","author":"Boujarzadeh","year":"2023","journal-title":"BMJ Open"},{"key":"2025121222470674000_13.9.e074705.7","doi-asserted-by":"crossref","DOI":"10.1016\/j.xagr.2023.100185","article-title":"Prediction of postpartum hemorrhage using statistical traditional analysis and machine learning approach","volume":"3","author":"Mehrnoush","year":"2023","journal-title":"AJOG Glob Rep"},{"key":"2025121222470674000_13.9.e074705.8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00421-1","article-title":"An analytics model for telecovas customers\u2019 basket clustering using ensemble learning approach","volume":"8","author":"Vahidi Farashah","year":"2021","journal-title":"J Big Data"},{"key":"2025121222470674000_13.9.e074705.9","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-02198-y","article-title":"Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth","volume":"11","author":"Akazawa","year":"2021","journal-title":"Sci Rep"},{"key":"2025121222470674000_13.9.e074705.10","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7647","article-title":"Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation","volume":"350","author":"Shamseer","year":"2015","journal-title":"BMJ"},{"key":"2025121222470674000_13.9.e074705.11","doi-asserted-by":"crossref","unstructured":"Page MJ , McKenzie J , Bossuyt P , et al . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. MetaArXiv [Preprint] 2020. doi:10.31222\/osf.io\/v7gm2","DOI":"10.31222\/osf.io\/v7gm2"},{"key":"2025121222470674000_13.9.e074705.12","doi-asserted-by":"publisher","DOI":"10.7326\/M18-1377"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2023-074705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:47:16Z","timestamp":1765608436000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2023-074705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":12,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9,11]]},"published-print":{"date-parts":[[2023,9]]}},"alternative-id":["10.1136\/bmjopen-2023-074705"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2023-074705","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"value":"2044-6055","type":"print"},{"value":"2044-6055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}