Background

Aniek obtained two bachelors in Econometrics and Economics (2017) and a master Business Analytics and Quantitative Marketing (2019) from Erasmus University Rotterdam. She wrote her master thesis on the causal effects of binary, continuous and multivariate treatments using propensity score methods. She worked as a research assistant at the Erasmus School of Health Policy and Management, where she investigated ethnic inequalities in deceased donor kidney allocation and the productivity of Dutch hospitals. Currently, she is pursuing a PhD at the department of Medical Informatics with a focus on explainable artificial intelligence. 

Publications

Markus, A. F., Rijnbeek, P. R., & Reps. J. M. (2022). Why predicting risk can’t identify ‘risk factors’: empirical assessment of model stability in machine learning across observational health databases. In Machine Learning for Healthcare 2022 (pp.1-25). PMLR. (links to full text and video).

Markus, A. F., Verhamme, K. M., Kors, J. A., & Rijnbeek, P. R. (2022). TreatmentPatterns: An R package to facilitate the standardized development and analysis of treatment patterns across disease domains. Computer Methods and Programs in Biomedicine, 107081. https://doi.org/10.1016/j.cmpb.2022.107081

Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655. https://doi.org/10.1016/j.jbi.2020.103655

Van de Klundert, J., van der Hagen, L., & Markus, A. (2021). Eliminating Transplant Waiting Time Inequities-with an application to Kidney Allocation in the USA. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.09.033

Markus, A. F., Williams, R. D., Yang, C., Duarte Salles, T., Falconer, T., Jonnagaddala, J., . . . Rijnbeek, P. R. (2020). Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network. BMC Medical Research Methodology, 22(1), 35. https://doi.org/10.1186/s12874-022-01505-z

Presentations

• Machine Learning for Healthcare 2022 & OHDSI Europe Symposium 2022: Why predicting risk can’t identify ‘risk factors’: empirical assessment of model stability in machine learning across observational health databases

Authors: Aniek Markus, Peter Rijnbeek, Jenna Reps

• BeRS-GSK Clinical Science Awards in Pulmonology: real-world treatment patterns of newly diagnosed asthma patients

Authors: Aniek Markus, Peter Rijnbeek, Jan Kors, Guy Brusselle, Katia Verhamme

Poster presentations 

• Treatment Patterns: An R package to analyze treatment patterns of a study population of interest (OHDSI 2021 Global Symposium)

Authors: Aniek F. Markus, Peter R. Rijnbeek, Jan A. Kors, Katia Verhamme

https://www.ohdsi.org/2021-global-symposium-showcase-70/

• Learning under constraints with EXPLORE (OHDSI 2021 Global Symposium)

Authors: Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek

https://www.ohdsi.org/2021-global-symposium-showcase-49/

• Real-world treatment patterns of newly diagnosed patients with asthma and/or COPD (ERS International Congress 2021, ICPE 2021) 

Authors: Aniek Markus, Peter Rijnbeek, Jan Kors, Guy Brusselle, Ed Burn, Daniel Prieto-Alhambra, Katia Verhamme

https://www.emjreviews.com/real-world-treatment-patterns-of-newly-diagnosed-patients-with-asthma-and-or-chronic-obstructive-pulmonary-disease-j160121/

• Real-world treatment patterns of newly diagnosed asthma patients (OHDSI 2020 Global Symposium)

Authors: Aniek Markus, Peter Rijnbeek, Jan Kors, Guy Brusselle, Katia Verhamme

https://www.ohdsi.org/2020-global-symposium-showcase-92/