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

Noemi Salmeri; Massimo Candiani; Paolo Ivo Cavoretto

doi:10.1186/s12884-023-05864-3

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

BMC Pregnancy Childbirth. 2023 Aug 2;23(1):554. doi: 10.1186/s12884-023-05864-3.

Authors

Noemi Salmeri^{1

2}, Massimo Candiani^{1

2}, Paolo Ivo Cavoretto^{3

4}

Affiliations

¹ Gynecology and Obstetrics Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy.
² Vita-Salute San Raffaele University, Milan, 20132, Italy.
³ Gynecology and Obstetrics Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy. cavoretto.paolo@hsr.it.
⁴ Vita-Salute San Raffaele University, Milan, 20132, Italy. cavoretto.paolo@hsr.it.

Abstract

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.

Keywords: Artificial intelligence; COVID-19; Machine learning; Pregnancy; SARS-CoV-2.

Publication types

Letter
Comment

MeSH terms

COVID-19*
Female
Humans
Machine Learning
Pregnancy
Pregnancy Outcome / epidemiology
Retrospective Studies
SARS-CoV-2*