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2020 – today
- 2024
[j11]Marco Piccininni
, Maximilian Wechsung, Ben Van Calster, Jessica L. Rohmann, Stefan Konigorski, Maarten van Smeden:
Understanding random resampling techniques for class imbalance correction and their consequences on calibration and discrimination of clinical risk prediction models. J. Biomed. Informatics 155: 104666 (2024)- [i5]Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster:
Understanding random forests and overfitting: a visualization and simulation study. CoRR abs/2402.18612 (2024) - [i4]Elena Albu, Shan Gao, Pieter Stijnen, Frank Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben Van Calster:
Comparison of static and dynamic random forests models for EHR data in the presence of competing risks: predicting central line-associated bloodstream infection. CoRR abs/2404.16127 (2024) - [i3]Elena Albu, Shan Gao, Laure Wynants, Ben Van Calster:
missForestPredict - Missing data imputation for prediction settings. CoRR abs/2407.03379 (2024) - 2023
- [j10]Paula Dhiman, Rebecca Whittle, Ben Van Calster, Marzyeh Ghassemi, Xiaoxuan Liu, Melissa D. McCradden, Karel G. M. Moons, Richard D. Riley, Gary S. Collins:
The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. Nat. Mac. Intell. 5(8): 816-817 (2023) - [j9]Anne A. H. de Hond, Vaibhavi B. Shah, Ilse M. J. Kant, Ben Van Calster, Ewout W. Steyerberg, Tina Hernandez-Boussard:
Perspectives on validation of clinical predictive algorithms. npj Digit. Medicine 6 (2023) - [i2]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:
Understanding metric-related pitfalls in image analysis validation. CoRR abs/2302.01790 (2023) - 2022
- [j8]Anne De Hond, Ben Van Calster, Ewout W. Steyerberg:
Commentary: Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins? Frontiers Digit. Health 4: 923944 (2022) - [j7]Ruben van den Goorbergh, Maarten van Smeden, Dirk Timmerman, Ben Van Calster:
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J. Am. Medical Informatics Assoc. 29(9): 1525-1534 (2022) - [i1]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:
Metrics reloaded: Pitfalls and recommendations for image analysis validation. CoRR abs/2206.01653 (2022)
2010 – 2019
- 2019
- [j6]Ben Van Calster, Laure Wynants, Dirk Timmerman, Ewout W. Steyerberg, Gary S. Collins:
Predictive analytics in health care: how can we know it works? J. Am. Medical Informatics Assoc. 26(12): 1651-1654 (2019) - 2015
- [j5]K. Van Hoorde, Sabine Van Huffel, Dirk Timmerman, Tom Bourne, Ben Van Calster:
A spline-based tool to assess and visualize the calibration of multiclass risk predictions. J. Biomed. Informatics 54: 283-293 (2015) - [j4]Andreas N. Strobl, Andrew J. Vickers, Ben Van Calster, Ewout W. Steyerberg, Robin J. Leach, Ian M. Thompson, Donna P. Ankerst:
Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators. J. Biomed. Informatics 56: 87-93 (2015) - 2012
- [j3]Ben Van Calster:
It takes time: A remarkable example of delayed recognition. J. Assoc. Inf. Sci. Technol. 63(11): 2341-2344 (2012)
2000 – 2009
- 2009
- [j2]Ben Van Calster, George Condous, Emma Kirk, Tom Bourne, Dirk Timmerman, Sabine Van Huffel:
An application of methods for the probabilistic three-class classification of pregnancies of unknown location. Artif. Intell. Medicine 46(2): 139-154 (2009) - [p1]Ben Van Calster, Olivier Gevaert, Caroline Van Holsbeke, Bart De Moor, Sabine Van Huffel, Dirk Timmerman:
Clinical decision support for ovarian tumor diagnosis using Bayesian models: Results from the IOTA study. Computational Intelligence and Bioengineering 2009: 111-128 - 2008
- [j1]Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Antonia C. Testa, Caroline Van Holsbeke, Ignace Vergote, Sabine Van Huffel:
Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery. Neural Comput. Appl. 17(5-6): 489-500 (2008) - [c6]Ben Van Calster, Sabine Van Huffel, Dirk Timmerman, Emma Kirk, Tom Bourne, George Condous:
Towards a Clinical Decision Support System for Pregnancies of Unknown Location. CBMS 2008: 581-583 - [c5]Ben Van Calster, Dirk Timmerman, Antonia C. Testa, Lil Valentin, Sabine Van Huffel:
Multi-class classification of ovarian tumors. ESANN 2008: 65-70 - [c4]Ben Van Calster, Vanya Van Belle, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel:
Multi-class AUC metrics and weighted alternatives. IJCNN 2008: 1390-1396 - 2007
- [c3]Ben Van Calster, Jan Luts, Johan A. K. Suykens, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel:
Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression. ICANN (2) 2007: 139-148 - [c2]M. S. Hane Aung, Paulo J. G. Lisboa, Terence A. Etchells, Antonia C. Testa, Ben Van Calster, Sabine Van Huffel, Lil Valentin, Dirk Timmerman:
Comparing Analytical Decision Support Models Through Boolean Rule Extraction: A Case Study of Ovarian Tumour Malignancy. ISNN (2) 2007: 1177-1186 - 2006
- [c1]Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Caroline Van Holsbeke, Sabine Van Huffel:
Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: A multi-center study. EMBC 2006: 5342-5345
Coauthor Index
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