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 (EMBC, 2006)

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 (ICANN (2), 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 (ISNN (2), 2007)

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 (CBMS, 2008)

Multi-class classification of ovarian tumors. - Ben Van Calster; Dirk Timmerman; Antonia C. Testa; Lil Valentin; Sabine Van Huffel (ESANN, 2008)

Multi-class AUC metrics and weighted alternatives. - Ben Van Calster; Vanya Van Belle; George Condous; Tom Bourne; Dirk Timmerman; Sabine Van Huffel (IJCNN, 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)

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)

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 (Computational Intelligence and Bioengineering, 2009)

It takes time: A remarkable example of delayed recognition. - Ben Van Calster (J. Assoc. Inf. Sci. Technol., 2012)

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)

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)

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)

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 001; Mauricio Reyes 001; Michael A. Riegler; Manuel Wiesenfarth; Michael Baumgartner 001; et al. (CoRR, 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)

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)

Understanding metric-related pitfalls in image analysis validation. - Annika Reinke; Minu Tizabi; Michael Baumgartner 001; Matthias Eisenmann; Doreen Heckmann-Nötzel; A. Emre Kavur; Tim Rädsch; Carole H. Sudre; Laura Ación; Michela Antonelli; Tal Arbel; Spyridon Bakas; et al. (CoRR, 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)

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., 2023)

Understanding random forests and overfitting: a visualization and simulation study. - Lasai Barreñada; Paula Dhiman; Dirk Timmerman; Anne-Laure Boulesteix; 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 E. Rademakers; Christel Janssens; Veerle Cossey; Yves Debaveye; Laure Wynants; Ben Van Calster (CoRR, 2024)

missForestPredict - Missing data imputation for prediction settings. - Elena Albu; Shan Gao; Laure Wynants; Ben Van Calster (CoRR, 2024)

Performance evaluation of predictive AI models to support medical decisions: Overview and guidance. - Ben Van Calster; Gary S. Collins; Andrew J. Vickers; Laure Wynants; Kathleen F. Kerr; Lasai Barreñada; Gaël Varoquaux; Karandeep Singh; Karel G. M. Moons; Tina Hernandez-Boussard; Dirk Timmerman; David J. Mclernon; et al. (CoRR, 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)

Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients - a practical guide. - Elena Albu; Shan Gao; Pieter Stijnen; Frank E. Rademakers; Bas C. T. van Bussel; Taya Collyer; Tina Hernandez-Boussard; Laure Wynants; Ben Van Calster (CoRR, 2025)

Critical Appraisal of Fairness Metrics in Clinical Predictive AI. - João Matos; Ben Van Calster; Leo Anthony Celi; Paula Dhiman; Judy Wawira Gichoya; Richard D. Riley; Chris Russell 001; Sara Khalid; Gary S. Collins (CoRR, 2025)