Background

Dr. Aniek Markus is a postdoctoral researcher with a focus on responsible AI development & large-scale evidence for women’s health. Her goal is to advance methods research to enable responsible, data-driven insights that improve healthcare for all. In her PhD thesis (2025) 'Opening the Black Box of Explainability', she investigated how different types of explanations can help to overcome the transparency problem of AI in health care. Furthermore, she has developed the TreatmentPatterns R package and is involved in several teaching activities.

Aniek is co-lead of the OHDSI Netherlands chapter, active contributor to OHDSI tools and packages, and has organized several tutorials/workshops during OHDSI symposia. She has been actively involved in EHDEN over the past years and is now contributing to DARWIN EU®. 

Previously, she 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.

Publications

Markus A. F., Rijnbeek P. R., Kors J. A., et al. Real-world treatment trajectories of adults with newly diagnosed asthma or COPD. BMJ Open Respiratory Research 2024;11(1):e002127 https://doi.org/10.1136/bmjresp-2023-002127.

Jacqueline Höllig*, Aniek F. Markus*, Jef de Slegte, Prachi Bagave. Semantic meaningfulness: Evaluating counterfactual approaches for real-world plausibility and feasibility. Explainable Artificial Intelligence 2023; Springer Nature Switzerland:636-59 https://doi.org/10.1007/978-3-031-44067-0_32.

Markus AF, Fridgeirsson EA, Kors JA, Verhamme K, Rijnbeek PR. Challenges of estimating global feature importance in real-world health care data. Caring is sharing–exploiting the value in data for health and innovation: IOS Press, 2023:1057-61.

Markus; AF, Arinze; JT, Verhamme KMC. Big data: Challenges and opportunities within respiratory care. Digital Respiratory Healthcare 2023:303 https://doi.org/10.1183/2312508X.erm10223.

Markus, A. F., Strauss, V. Y., Burn, E., Li, X., Delmestri, A., Reich, C., ... & Jödicke, A. M. (2023). Characterising the treatment of thromboembolic events after COVID-19 vaccination in 4 European countries and the US: An international network cohort study. Frontiers in Pharmacology, 14, 1118203. https://doi.org/10.3389/fphar.2023.1118203.

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. https://proceedings.mlr.press/v182/markus22a/markus22a.pdf.

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

Dohmen, P., van Ineveld, M., Markus, A., van der Hagen, L., & van de Klundert, J. (2022). Does competition improve hospital performance: a DEA based evaluation from the Netherlands. The European Journal of Health Economics, 1-19. https://doi.org/10.1007/s10198-022-01529-8

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

Williams, R. D.*, Markus, A. F.*, 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

• Data Analytics x CAIRELab Seminar Explainable AI 2024.

• Oxford RWE Summer School 2023.

• Medical Informatics Europe 2023: Challenges of Estimating Global Feature Importance in Real-World Health Care Data.

• 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.

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

Poster presentations 

• Understanding the Size of the Feature Importance Disagreement Problem in Real-World Data (ICML Workshop 2023 - IMLH)

Authors: Aniek F. Markus, Egill A. Fridgeirsson, Jan A. Kors, Katia M.C. Verhamme, Jenna M. Reps, Peter R. Rijnbeek

https://openreview.net/forum?id=FKjFUEV63f

• Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility

  (ICML Workshop 2023 - Counterfactuals in Minds & Machines)

Authors: Aniek F. Markus*, Jacqueline Höllig*, Jef de Slegte, Prachi Bagave

https://drive.google.com/file/d/1tD6nLdQQzvE64ktEwswi1kMSnT6rPKuQ/view

• Explaining patient-level prediction models using permutation feature importance and SHAP (OHDSI 2022 Global Symposium)

Authors: Aniek F. Markus, Egill A. Fridgeirsson, Jan A. Kors, Katia M.C. Verhamme, Peter R. Rijnbeek

https://www.ohdsi.org/2022showcase-74/

• 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/