Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics.

Journal: Obstetrics and gynecology
Published Date: Jun 1, 2025

Abstract

OBJECTIVE: To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.

Authors

  • Sarah Miller
    IBM Research, USA.
  • Deirdre Lyell
  • Ivana Marić
    Department of Pediatrics, Stanford University, Stanford, California, USA.
  • Samuel Lancaster
  • Karl Sylvester
  • Kévin Contrepois
    Department of Genetics, Stanford University School of Medicine, Stanford, Calif.
  • Samantha Kruger
  • Jordan Burgess
  • David Stevenson
    Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA.
  • Nima Aghaeepour
    Departments of Anesthesiology, Pain, and Peri-operative Medicine and Biomedical Data Sciences, Stanford University, Stanford, CA, USA.
  • Michael Snyder
    Department of Genetics, Stanford University School of Medicine, Stanford, Calif.
  • Elisa Zhang
  • Keyla Badillo
  • Robert Silver
  • Brett D Einerson
  • Katherine Bianco

Keywords

External Resources

Popular Topics
Recent Journals