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

Vista sencilla de ítem
dc.contributor.authorBertini, Ayleen
dc.contributor.authorSalas, Rodrigo
dc.contributor.authorChabert, Steren
dc.contributor.authorSobrevia, Luis
dc.contributor.authorPardo, Fabián
dc.date.accessioned2022-11-30T02:46:12Z
dc.date.available2022-11-30T02:46:12Z
dc.date.issued2022
dc.description.abstractIntroduction: 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.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameBertini_Usi2022.pdf
dc.identifier.citationBertini A, Salas R, Chabert S, Sobrevia L and Pardo F (2022) Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front. Bioeng. Biotechnol. 9:780389. doi: 10.3389/fbioe.2021.780389en_ES
dc.identifier.doihttps://doi.org/10.3389/fbioe.2021.780389
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7245
dc.languageen
dc.publisherFrontiers
dc.sourceFrontiers in Bioengineering and Biotechnology
dc.subjectPERINATAL COMPLICATIONSen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.subjectPREGNANCYen_ES
dc.subjectARTIFICIAL INTELLIGENCEen_ES
dc.subjectPREDICTIVE TOOLen_ES
dc.subjectPREDICTION MODELen_ES
dc.titleUsing Machine Learning to Predict Complications in Pregnancy: A Systematic Review
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Biomedica

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Bertini_Usi2022.pdf
Tamaño:
1.32 MB
Formato:
Adobe Portable Document Format
Descargar

Colecciones

Artículos investigadores UV