4 citations · 13 across the 8 of their papers we have counts for
9 papers
Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection
Shan Gao, Elena Albu, Pieter Stijnen +6
Electronic health records (EHR) often contain varying levels of missing data. This study compared different imputation strategies to identify the most suitable approach for predict…
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…
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 +6
Dynamic predictive modelling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend h…
The continuous net benefit: Assessing the clinical utility of prediction models when informing a continuum of decisions
Jose Benitez-Aurioles, Laure Wynants, Niels Peek +3
Clinical prognostic models help inform decision-making by estimating a patient's risk of experiencing an outcome in the future. The net benefit is increasingly being used to assess…
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…
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Richard D Riley, Gary S Collins, Rebecca Whittle +11
When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instabil…
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 +6
Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common…
A comparison of regression models for static and dynamic prediction of a prognostic outcome during admission in electronic health care records
Shan Gao, Elena Albu, Hein Putter +7
Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We a…