The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. - Aniek F. Markus; Jan A. Kors; Peter R. Rijnbeek (J. Biomed. Informatics, 2021)

Why predicting risk can't identify 'risk factors': empirical assessment of model stability in machine learning across observational health databases. - Aniek F. Markus; Peter R. Rijnbeek; Jenna Marie Reps (MLHC, 2022)

TreatmentPatterns: An R package to facilitate the standardized development and analysis of treatment patterns across disease domains. - Aniek F. Markus; Katia M. C. Verhamme; Jan A. Kors; Peter R. Rijnbeek (Comput. Methods Programs Biomed., 2022)

Eliminating transplant waiting time inequities - With an application to kidney allocation in the USA. - Joris van de Klundert; Liana van der Hagen; Aniek F. Markus (Eur. J. Oper. Res., 2022)

Use of unstructured text in prognostic clinical prediction models: a systematic review. - Tom M. Seinen; Egill A. Fridgeirsson; Solomon Ioannou; Daniel Jeannetot; Luis H. John; Jan A. Kors; Aniek F. Markus; Victor Pera; Alexandros Rekkas; Ross D. Williams; Cynthia Yang; Erik M. van Mulligen; et al. (J. Am. Medical Informatics Assoc., 2022)

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review. - Cynthia Yang; Jan A. Kors; Solomon Ioannou; Luis H. John; Aniek F. Markus; Alexandros Rekkas; Maria A. J. de Ridder; Tom M. Seinen; Ross D. Williams; Peter R. Rijnbeek (J. Am. Medical Informatics Assoc., 2022)

Challenges of Estimating Global Feature Importance in Real-World Health Care Data. - Aniek F. Markus; Egill A. Fridgeirsson; Jan A. Kors; Katia M. C. Verhamme; Peter R. Rijnbeek (MIE, 2023)

Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility. - Jacqueline Höllig; Aniek F. Markus; Jef de Slegte; Prachi Bagave (xAI (2), 2023)