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

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