Statistical analyses and machine learning models: Insights about the relation between...

Diagnostic information and data on occurrence of cardiovascular complications in COVID-19 patients is rapidly growing but is distributed over different clinical locations. In order to provide the most accurate insights about the relation between cardiovascular history and related complications in COVID-19 patients, statistical analyses and machine learning models need to be kept up to date in real time. This will not be possible by continuously collecting data manually from different locations. The FAIR Data for Capacity project will build FAIR data stations and automatic data extraction pipelines for defined sets of clinical data as part of a distributed learning infrastructure. This will provide insight in the incidence of cardiovascular complications in patients with COVID-19, and the vulnerability and clinical course of COVID-19 in patients with an underlying cardiovascular disease.

Research Team: Dr. Andre Dekker  (Maastricht University, Personal Health Train – PHT), Dr. Rick van Nuland (Lygature, HealthRI), Prof. Folkert Asselbergs (UMC Utrecht, Dutch Cardiovascular Alliance – DCVA), Dr. Mira Staphorst (Hartstichting, DCVA)eScience Research Engineers: Dr. Lars Ridder, Djura Smits, MSc


  • A.L.A.J. Dekker
  • Djura Smits
    Netherlands eScience Center
  • Lars Ridder
    Netherlands eScience Center
Contact person
Djura Smits
Netherlands eScience Center