Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth

Abstract Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Toky...

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Autores principales:	Munetoshi Akazawa, Kazunori Hashimoto, Noda Katsuhiko, Yoshida Kaname
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/abe19006ac134c9c8f8e746be53455fd
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spelling	oai:doaj.org-article:abe19006ac134c9c8f8e746be53455fd2021-11-21T12:23:47ZMachine learning approach for the prediction of postpartum hemorrhage in vaginal birth10.1038/s41598-021-02198-y2045-2322https://doaj.org/article/abe19006ac134c9c8f8e746be53455fd2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02198-yhttps://doaj.org/toc/2045-2322Abstract Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to predict a postpartum hemorrhage defined as a blood loss of > 1000 mL. We constructed five machine learning models and a deep learning model consisting of neural networks with two layers after applying the ensemble learning of five machine learning classifiers, namely, logistic regression, a support vector machine, random forest, boosting trees, and decision tree. For an evaluation of the performance, we applied the area under the curve of the receiver operating characteristic (AUC), the accuracy, false positive rate (FPR) and false negative rate (FNR). The importance of each variable was evaluated through a comparison of the feature importance calculated using a Boosted tree. A total of 9,894 patients who underwent vaginal birth were enrolled in the study, including 188 cases (1.9%) with blood loss of > 1000 mL. The best learning model predicted postpartum hemorrhage with an AUC of 0.708, an accuracy of 0.686, FPR of 0.312, and FNR of 0.398. The analysis of the importance of the variables showed that pregnant gestation of labor, the maternal weight upon admission of labor, and the maternal weight before pregnancy were considered to be weighted factors. Machine learning model can predict postpartum hemorrhage during vaginal delivery. Further research should be conducted to analyze appropriate variables and prepare big data, such as hundreds of thousands of cases.Munetoshi AkazawaKazunori HashimotoNoda KatsuhikoYoshida KanameNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Munetoshi Akazawa Kazunori Hashimoto Noda Katsuhiko Yoshida Kaname Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
description	Abstract Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to predict a postpartum hemorrhage defined as a blood loss of > 1000 mL. We constructed five machine learning models and a deep learning model consisting of neural networks with two layers after applying the ensemble learning of five machine learning classifiers, namely, logistic regression, a support vector machine, random forest, boosting trees, and decision tree. For an evaluation of the performance, we applied the area under the curve of the receiver operating characteristic (AUC), the accuracy, false positive rate (FPR) and false negative rate (FNR). The importance of each variable was evaluated through a comparison of the feature importance calculated using a Boosted tree. A total of 9,894 patients who underwent vaginal birth were enrolled in the study, including 188 cases (1.9%) with blood loss of > 1000 mL. The best learning model predicted postpartum hemorrhage with an AUC of 0.708, an accuracy of 0.686, FPR of 0.312, and FNR of 0.398. The analysis of the importance of the variables showed that pregnant gestation of labor, the maternal weight upon admission of labor, and the maternal weight before pregnancy were considered to be weighted factors. Machine learning model can predict postpartum hemorrhage during vaginal delivery. Further research should be conducted to analyze appropriate variables and prepare big data, such as hundreds of thousands of cases.
format	article
author	Munetoshi Akazawa Kazunori Hashimoto Noda Katsuhiko Yoshida Kaname
author_facet	Munetoshi Akazawa Kazunori Hashimoto Noda Katsuhiko Yoshida Kaname
author_sort	Munetoshi Akazawa
title	Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
title_short	Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
title_full	Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
title_fullStr	Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
title_full_unstemmed	Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
title_sort	machine learning approach for the prediction of postpartum hemorrhage in vaginal birth
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/abe19006ac134c9c8f8e746be53455fd
work_keys_str_mv	AT munetoshiakazawa machinelearningapproachforthepredictionofpostpartumhemorrhageinvaginalbirth AT kazunorihashimoto machinelearningapproachforthepredictionofpostpartumhemorrhageinvaginalbirth AT nodakatsuhiko machinelearningapproachforthepredictionofpostpartumhemorrhageinvaginalbirth AT yoshidakaname machinelearningapproachforthepredictionofpostpartumhemorrhageinvaginalbirth
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Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth

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