Open AccessMachine Learning and Statistical Models to Predict Postpartum Hemorrhage
Author(s) -
Kartik K. Venkatesh,
Robert A. Strauss,
Chad A. Grotegut,
R Philip Heine,
Nancy C. Chescheir,
Jeffrey S. A. Stringer,
David M. Stamilio,
Katherine M Menard,
J. Eric Jelovsek
Publication year - 2020
Publication title -
obstetrics and gynecology (new york. 1953. online)/obstetrics and gynecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.664
H-Index - 220
eISSN - 1873-233X
pISSN - 0029-7844
DOI - 10.1097/aog.0000000000003759
Subject(s) - medicine , logistic regression , lasso (programming language) , random forest , discriminative model , statistics , statistic , press statistic , regression , concordance , regression analysis , machine learning , mathematics , ancillary statistic , computer science , f test , world wide web
To predict a woman's risk of postpartum hemorrhage at labor admission using machine learning and statistical models.