Machine Learning and Statistical Models to Predict Postpartum Hemorrhage.

ABSTRACT:

Objective

To predict a woman's risk of postpartum hemorrhage at labor admission using machine learning and statistical models.

Methods

Predictive models were constructed and compared using data from 10 of 12 sites in the U.S. Consortium for Safe Labor Study (2002-2008) that consistently reported estimated blood loss at delivery. The outcome was postpartum hemorrhage, defined as an estimated blood loss at least 1,000 mL. Fifty-five candidate risk factors routinely available on labor admission were considered. We used logistic regression with and without lasso regularization (lasso regression) as the two statistical models, and random forest and extreme gradient boosting as the two machine learning models to predict postpartum hemorrhage. Model performance was measured by C st

SUBMITTER: Venkatesh KK 

PROVIDER: S-EPMC7236480 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature