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

Introduction: 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 child...

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Autores principales: Bertini, Ayleen, Salas, Rodrigo, Chabert, Steren, Sobrevia, Luis, Pardo, Fabián
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807522/
https://www.ncbi.nlm.nih.gov/pubmed/35127665
http://dx.doi.org/10.3389/fbioe.2021.780389
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author Bertini, Ayleen
Salas, Rodrigo
Chabert, Steren
Sobrevia, Luis
Pardo, Fabián
author_facet Bertini, Ayleen
Salas, Rodrigo
Chabert, Steren
Sobrevia, Luis
Pardo, Fabián
author_sort Bertini, Ayleen
collection PubMed
description Introduction: 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.
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spelling pubmed-88075222022-02-03 Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review Bertini, Ayleen Salas, Rodrigo Chabert, Steren Sobrevia, Luis Pardo, Fabián Front Bioeng Biotechnol Bioengineering and Biotechnology Introduction: 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. Frontiers Media S.A. 2022-01-19 /pmc/articles/PMC8807522/ /pubmed/35127665 http://dx.doi.org/10.3389/fbioe.2021.780389 Text en Copyright © 2022 Bertini, Salas, Chabert, Sobrevia and Pardo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Bertini, Ayleen
Salas, Rodrigo
Chabert, Steren
Sobrevia, Luis
Pardo, Fabián
Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title_full Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title_fullStr Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title_full_unstemmed Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title_short Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review
title_sort using machine learning to predict complications in pregnancy: a systematic review
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807522/
https://www.ncbi.nlm.nih.gov/pubmed/35127665
http://dx.doi.org/10.3389/fbioe.2021.780389
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