143 citations · 162 across the 5 of their papers we have counts for
6 papers
Optimizing for the decision not the prediction: an exploration of Smooth Net Benefit as a training objective
Koen M. F. Gorgels, Lasai Barreñada, Maarten van Smeden +3
Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decisions may depend on specific ris…
Code Sharing In Prediction Model Research: A Scoping Review
Thomas Sounack, Raffaele Giancotti, Catherine A. Gao +8
Analytical code is essential for reproducing diagnostic and prognostic prediction model research, yet code availability in the published literature remains limited. While the TRIPO…
The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Lasai Barreñada, Ewout W Steyerberg, Dirk Timmerman +3
Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view re…
Clustered Flexible Calibration Plots For Binary Outcomes Using Random Effects Modeling
Lasai Barreñada, Bavo D. C. Campo, Laure Wynants +1
Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment o…
Performance evaluation of predictive AI models to support medical decisions: Overview and guidance
Ben Van Calster, Gary S. Collins, Andrew J. Vickers +11
A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is…
Understanding overfitting in random forest for probability estimation: a visualization and simulation study
Lasai Barreñada, Paula Dhiman, Dirk Timmerman +2
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although…