Steven W. J. Nijman

Real-time imputation of missing predictor values improved the application of prediction models in daily practice

T. Katrien J. Groenhof, Folkert W. Asselbergs, Thomas P. A. Debray, 2021 .

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

G. Collins, L. Hooft, R. Bajpai, 2021, BMJ.

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

E. Steyerberg, L. Hooft, N. Chavannes, 2022, npj Digital Medicine.

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

G. Collins, L. Hooft, R. Bajpai, 2021, BMC Medical Research Methodology.

Real-time imputation of missing predictor values in clinical practice

Karel GM Moons, T Katrien J Groenhof, Michiel L Bots, 2020, European heart journal. Digital health.

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

G. Collins, L. Hooft, R. Bajpai, 2020, BMJ Open.

Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models.

L. Hooft, R. Riley, K. Moons, 2023, Journal of clinical epidemiology.

The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance

M. van Boven, H. D. de Melker, I. Van Walle, 2023, Scientific data.

Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets.

S. Denaxas, F. Asselbergs, K. Snell, 2021, Journal of clinical epidemiology.