24 citations · 49 across the 7 of their papers we have counts for
8 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…
Causally-interpretable meta-analysis using aggregate data
Qingyang Shi, Wouter van Amsterdam, Sacha la Bastide-van Gemert +2
Evidence syntheses and meta-analyses are used to inform clinical practice guidelines and health economic evaluations. However, heterogeneity of treatment effects poses a significan…
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…
When accurate prediction models yield harmful self-fulfilling prophecies
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe +2
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisio…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
From algorithms to action: improving patient care requires causality
Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff +2
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are…
Conditional average treatment effect estimation with marginally constrained models
Wouter A. C. van Amsterdam, Rajesh Ranganath
Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population. Estimates of the conditional…
Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning
Wouter A. C. van Amsterdam, Marinus J. C. Eijkemans
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to b…