Explanatory Data Analysis group

Marieke Vinkenoog

Marieke Vinkenoog
Marieke Vinkenoog
Lecturer & PhD candidate

Lecturer & PhD candidate Google Scholar profile LinkedIn profile Twitter profile

Marieke is a PhD student working on the 'Data Science for State-of-the-Art Blood Banking' (BloodStart) project. This project is a collaboration between Sanquin and Leiden University, and aims to significantly improve the prediction of donor medical test outcomes and donor behaviour. Data of the past 20 years is available, on around 12 million donations. The BloodStart project will deliver enhanced data-driven models and evidence-based donor management strategies that will maximise the effectiveness of resources and minimise donor loss. The main supervisors of the project will be Mart Janssen from Sanquin and Matthijs van Leeuwen and Aske Plaat from Leiden University.

Marieke completed the Master Statistical Science with a specialisation in Data Science in 2018 at Leiden University. Her Bachelor's degree is in Biology, and she enjoys combining knowledge from both scientific areas in her research.

Selected recent publications

Vinkenoog, M, van Leeuwen, M & Janssen, M Explainable hemoglobin deferral predictions using machine learning models: interpretation and consequences for the blood supply. Vox Sanguinis
Vinkenoog, M, Steenhuis, M, ten Brinke, A, van Hasselt, C, Janssen, M, van Leeuwen, M, Swaneveld, F, Vrielink, H, van de Watering, L, Quee, F, van cen Hurk, K, Rispens, T, Hogema, B & van der Schoot, E Associations between symptoms, donor characteristics and IgG antibody response in 2082 COVID-19 convalescent plasma donors. Frontiers in Immunology, Frontiers
Vinkenoog, M, van den Hurk, K, van Kraaij, M, van Leeuwen, M & Janssen, M First results of a ferritin-based blood donor deferral policy in the Netherlands. Transfusion vol.60(8), pp 1785-1792, Wiley, 2020.
Vinkenoog, M, Janssen, M & van Leeuwen, M Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories. In: Proceedings of 4th Workshop on Advanced Analytics and Learning on Temporal Data at ECMLPKDD 2019, Springer, 2019.