Early diagnosis of persons with von Willebrand disease using a machine learning algorithm and real-world data.

Journal: Expert review of hematology
Published Date:

Abstract

BACKGROUND: Von Willebrand disease (VWD) is underdiagnosed, often delaying treatment. VWD claims coding is limited and includes no severity qualifiers; improved identification methods for VWD are needed. The aim of this study is to identify and characterize undiagnosed symptomatic persons with VWD in the US from medical insurance claims using predictive machine learning (ML) models.

Authors

  • Robert F Sidonio
    Aflac Cancer and Blood Disorders Center, Egleston Hospital, Emory University School of Medicine, Atlanta, GA, USA.
  • Anan Lu
    Life Sciences Practice, Charles River Associates, Boston, MA, USA.
  • Sarah Hale
    US Medical, Takeda Pharmaceuticals USA Inc, Lexington, MA, USA.
  • Jorge Caicedo
    US Medical, Takeda Pharmaceuticals USA Inc, Lexington, MA, USA.
  • Mike Bullano
    US Medical, Takeda Pharmaceuticals USA Inc, Lexington, MA, USA.
  • Shan Xing
    US Medical, Takeda Pharmaceuticals USA Inc, Lexington, MA, USA.