Predictive Analytics and Modeling Employing Machine Learning Technology: The Next Step in Data Sharing, Analysis, and Individualized Counseling Explored With a Large, Prospective Prenatal Hydronephrosis Database.

Journal: Urology
Published Date:

Abstract

OBJECTIVE: To explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN).

Authors

  • Armando J Lorenzo
    Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Mandy Rickard
    Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Luis H Braga
    Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada; McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada; McMaster Pediatric Surgery Research Collaborative, McMaster University, Hamilton, Ontario, Canada.
  • Yanbo Guo
    McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada.
  • John-Paul Oliveria
    Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada.