Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidad de Valparaíso-
Autor(es): dc.contributorInstituto Milenio Intelligent Healthcare Engineering-
Autor(es): dc.contributorPontificia Universidad Católica de Chile-
Autor(es): dc.contributorUniversidad de Sevilla-
Autor(es): dc.contributorUniversity of Queensland-
Autor(es): dc.contributorUniversity Medical Center Groningen-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSchool of Medicine and Health Sciences-
Autor(es): dc.creatorBertini, Ayleen-
Autor(es): dc.creatorSalas, Rodrigo-
Autor(es): dc.creatorChabert, Steren-
Autor(es): dc.creatorSobrevia, Luis-
Autor(es): dc.creatorPardo, Fabián-
Data de aceite: dc.date.accessioned2025-08-21T19:07:24Z-
Data de disponibilização: dc.date.available2025-08-21T19:07:24Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-01-18-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fbioe.2021.780389-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/223400-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/223400-
Descrição: dc.descriptionIntroduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.-
Descrição: dc.descriptionUniversidad de Valparaíso-
Descrição: dc.descriptionMetabolic Diseases Research Laboratory (MDRL) Interdisciplinary Center for Research Territorial Health of the Aconcagua Valley (CIISTe Aconcagua) Center for Biomedical Research (CIB) Universidad de Valparaíso-
Descrição: dc.descriptionPhD Program Doctorado en Ciencias e Ingeniería para La Salud Faculty of Medicine Universidad de Valparaíso-
Descrição: dc.descriptionSchool of Biomedical Engineering Faculty of Engineering Universidad de Valparaíso-
Descrição: dc.descriptionCentro de Investigación y Desarrollo en INGeniería en Salud – CINGS Universidad de Valparaíso-
Descrição: dc.descriptionInstituto Milenio Intelligent Healthcare Engineering-
Descrição: dc.descriptionCellular and Molecular Physiology Laboratory (CMPL) Division of Obstetrics and Gynaecology School of Medicine Faculty of Medicine Pontificia Universidad Católica de Chile-
Descrição: dc.descriptionDepartment of Physiology Faculty of Pharmacy Universidad de Sevilla-
Descrição: dc.descriptionUniversity of Queensland Centre for Clinical Research (UQCCR) Faculty of Medicine and Biomedical Sciences University of Queensland-
Descrição: dc.descriptionDepartment of Pathology and Medical Biology University of Groningen University Medical Center Groningen-
Descrição: dc.descriptionMedical School (Faculty of Medicine) São Paulo State University (UNESP)-
Descrição: dc.descriptionTecnologico de Monterrey Eutra The Institute for Obesity Research School of Medicine and Health Sciences-
Descrição: dc.descriptionSchool of Medicine Faculty of Medicine Universidad de Valparaíso, Campus San Felipe-
Descrição: dc.descriptionMedical School (Faculty of Medicine) São Paulo State University (UNESP)-
Descrição: dc.descriptionUniversidad de Valparaíso: UVA20993-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Bioengineering and Biotechnology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectperinatal complications-
Palavras-chave: dc.subjectprediction model-
Palavras-chave: dc.subjectpredictive tool-
Palavras-chave: dc.subjectpregnancy-
Título: dc.titleUsing Machine Learning to Predict Complications in Pregnancy: A Systematic Review-
Tipo de arquivo: dc.typevídeo-
Aparece nas coleções:Repositório Institucional - Unesp

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