Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

Journal: American journal of obstetrics & gynecology MFM
PMID:

Abstract

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk factors rather than predictive models.

Authors

  • Benjamin J Lengerich
    Massachusetts Institute of Technology, Cambridge, MA USA.
  • Rich Caruana
    Microsoft Research, Redmond, WA USA.
  • Ian Painter
    Foundation for Healthcare Quality, Seattle, WA USA.
  • William B Weeks
    AI for Good Lab, Microsoft Corporation, Redmond, WA, United States.
  • Kristin Sitcov
    Foundation for Health Care Quality, Seattle, WA (Painter, Sitcov and Souter).
  • Vivienne Souter
    Foundation for Healthcare Quality, Seattle, WA USA.