Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis

od Plate, Joost D. J., van de Leur, Rutger R., Leenen, Luke P. H., Hietbrink, Falco, Peelen, Linda M., Eijkemans, M. J. C.
Wydane w BMC Med Res Methodol (2019)

Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks

od van de Leur, Rutger R., Blom, Lennart J., Gavves, Efstratios, Hof, Irene E., van der Heijden, Jeroen F., Clappers, Nick C., Doevendans, Pieter A., Hassink, Rutger J., van Es, René
Wydane w J Am Heart Assoc (2020)

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers

od van de Leur, Rutger R., Taha, Karim, Bos, Max N., van der Heijden, Jeroen F., Gupta, Deepak, Cramer, Maarten J., Hassink, Rutger J., van der Harst, Pim, Doevendans, Pieter A., Asselbergs, Folkert W., van Es, René
Wydane w Circ Arrhythm Electrophysiol (2021)

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

od van de Leur, Rutger R, Boonstra, Machteld J, Bagheri, Ayoub, Roudijk, Rob W, Sammani, Arjan, Taha, Karim, Doevendans, Pieter AFM, van der Harst, Pim, van Dam, Peter M, Hassink, Rutger J, van Es, René, Asselbergs, Folkert W
Wydane w Arrhythm Electrophysiol Rev (2020)