4 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
missForestPredict -- Missing data imputation for prediction settings
Elena Albu, Shan Gao, Laure Wynants +1
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestP…
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…
How to develop, externally validate, and update multinomial prediction models
Celina K Gehringer, Glen P Martin, Ben Van Calster +3
Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. Howe…