Ben Van Calster

According to our database, Ben Van Calster authored at least 23 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Understanding random resampling techniques for class imbalance correction and their consequences on calibration and discrimination of clinical risk prediction models.

Marco Piccininni, Maximilian Wechsung, Ben Van Calster, Jessica L. Rohmann, Stefan Konigorski, Maarten van Smeden

J. Biomed. Informatics, 2024

missForestPredict - Missing data imputation for prediction settings.

Elena Albu, Shan Gao, Laure Wynants, Ben Van Calster

CoRR, 2024

Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection.

Elena Albu, Shan Gao, Pieter Stijnen, Frank Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben Van Calster

CoRR, 2024

Understanding random forests and overfitting: a visualization and simulation study.

Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster

CoRR, 2024

2023

The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols.

Paula Dhiman, Rebecca Whittle, Ben Van Calster, Marzyeh Ghassemi, Xiaoxuan Liu, Melissa D. McCradden, Karel G. M. Moons, Richard D. Riley, Gary S. Collins

Nat. Mac. Intell., August, 2023

Perspectives on validation of clinical predictive algorithms.

Anne A. H. de Hond, Vaibhavi B. Shah, Ilse M. J. Kant, Ben Van Calster, Ewout W. Steyerberg, Tina Hernandez-Boussard

npj Digit. Medicine, 2023

Understanding metric-related pitfalls in image analysis validation.

Annika Reinke, Minu Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew B. Blaschko, Florian Büttner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir M. Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

CoRR, 2023

2022

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression.

Ruben van den Goorbergh, Maarten van Smeden, Dirk Timmerman, Ben Van Calster

J. Am. Medical Informatics Assoc., 2022

Commentary: Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?

Anne De Hond, Ben Van Calster, Ewout W. Steyerberg

Frontiers Digit. Health, 2022

Metrics reloaded: Pitfalls and recommendations for image analysis validation.

Lena Maier-Hein, Annika Reinke, Evangelia Christodoulou, Ben Glocker, Patrick Godau, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Minu Dietlinde Tizabi, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Annette Kopp-Schneider, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, David Moher, Karel G. M. Moons, Henning Müller, Felix Nickel, Brennan Nichyporuk, Jens Petersen, Nasir M. Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clarisa Sánchez Gutiérrez, Shravya Shetty, Maarten van Smeden, Carole H. Sudre, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Paul F. Jäger

CoRR, 2022

2019

Predictive analytics in health care: how can we know it works?

Ben Van Calster, Laure Wynants, Dirk Timmerman, Ewout W. Steyerberg, Gary S. Collins

J. Am. Medical Informatics Assoc., 2019

2015

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators.

Andreas N. Strobl, Andrew J. Vickers, Ben Van Calster, Ewout W. Steyerberg, Robin J. Leach, Ian M. Thompson, Donna P. Ankerst

J. Biomed. Informatics, 2015

A spline-based tool to assess and visualize the calibration of multiclass risk predictions.

K. Van Hoorde, Sabine Van Huffel, Dirk Timmerman, Tom Bourne, Ben Van Calster

J. Biomed. Informatics, 2015

2012

It takes time: A remarkable example of delayed recognition.

Ben Van Calster

J. Assoc. Inf. Sci. Technol., 2012

2009

Clinical decision support for ovarian tumor diagnosis using Bayesian models: Results from the IOTA study.

Ben Van Calster, Olivier Gevaert, Caroline Van Holsbeke, Bart De Moor, Sabine Van Huffel, Dirk Timmerman

Proceedings of the Computational Intelligence and Bioengineering, 2009

An application of methods for the probabilistic three-class classification of pregnancies of unknown location.

Ben Van Calster, George Condous, Emma Kirk, Tom Bourne, Dirk Timmerman, Sabine Van Huffel

Artif. Intell. Medicine, 2009

2008

Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery.

Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Antonia C. Testa, Caroline Van Holsbeke, Ignace Vergote, Sabine Van Huffel

Neural Comput. Appl., 2008

Multi-class AUC metrics and weighted alternatives.

Ben Van Calster, Vanya Van Belle, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel

Proceedings of the International Joint Conference on Neural Networks, 2008

Multi-class classification of ovarian tumors.

Ben Van Calster, Dirk Timmerman, Antonia C. Testa, Lil Valentin, Sabine Van Huffel

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Towards a Clinical Decision Support System for Pregnancies of Unknown Location.

Ben Van Calster, Sabine Van Huffel, Dirk Timmerman, Emma Kirk, Tom Bourne, George Condous

Proceedings of the Twenty-First IEEE International Symposium on Computer-Based Medical Systems, 2008

2007

Comparing Analytical Decision Support Models Through Boolean Rule Extraction: A Case Study of Ovarian Tumour Malignancy.

M. S. Hane Aung, Paulo J. G. Lisboa, Terence A. Etchells, Antonia C. Testa, Ben Van Calster, Sabine Van Huffel, Lil Valentin, Dirk Timmerman

Proceedings of the Advances in Neural Networks, 2007

Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression.

Ben Van Calster, Jan Luts, Johan A. K. Suykens, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel

Proceedings of the Artificial Neural Networks, 2007

2006

Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: A multi-center study.

Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Caroline Van Holsbeke, Sabine Van Huffel

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006