Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach

SARS-CoV-2 infection poses a significant risk increase for adverse pregnancy outcomes both from maternal and fetal sides. A recent publication in BMC Pregnancy and Childbirth presented a machine learning algorithm to predict this risk. This commentary will discuss potential implications and applicat...

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Detalles Bibliográficos
Autores principales: Salmeri, Noemi, Candiani, Massimo, Cavoretto, Paolo Ivo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394926/
https://www.ncbi.nlm.nih.gov/pubmed/37532988
http://dx.doi.org/10.1186/s12884-023-05864-3
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author Salmeri, Noemi
Candiani, Massimo
Cavoretto, Paolo Ivo
author_facet Salmeri, Noemi
Candiani, Massimo
Cavoretto, Paolo Ivo
author_sort Salmeri, Noemi
collection PubMed
description SARS-CoV-2 infection poses a significant risk increase for adverse pregnancy outcomes both from maternal and fetal sides. A recent publication in BMC Pregnancy and Childbirth presented a machine learning algorithm to predict this risk. This commentary will discuss potential implications and applications of this study for future global health policies.
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spelling pubmed-103949262023-08-03 Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach Salmeri, Noemi Candiani, Massimo Cavoretto, Paolo Ivo BMC Pregnancy Childbirth Comment SARS-CoV-2 infection poses a significant risk increase for adverse pregnancy outcomes both from maternal and fetal sides. A recent publication in BMC Pregnancy and Childbirth presented a machine learning algorithm to predict this risk. This commentary will discuss potential implications and applications of this study for future global health policies. BioMed Central 2023-08-02 /pmc/articles/PMC10394926/ /pubmed/37532988 http://dx.doi.org/10.1186/s12884-023-05864-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Comment
Salmeri, Noemi
Candiani, Massimo
Cavoretto, Paolo Ivo
Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title_full Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title_fullStr Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title_full_unstemmed Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title_short Commentary: Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2 by a machine learning approach
title_sort commentary: predicting adverse outcomes in pregnant patients positive for sars-cov-2 by a machine learning approach
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394926/
https://www.ncbi.nlm.nih.gov/pubmed/37532988
http://dx.doi.org/10.1186/s12884-023-05864-3
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