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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
Published:
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portfolio
Portfolio item number 1
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publications
Delirium in critically ill patients.
Published in Handbook of Clinical Neurology, 2017
Recommended citation: Slooter AJC, Van De Leur RR, Zaal IJ. Delirium in critically ill patients. Handb Clin Neurol. 2017;141:449–466.
3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging
Published in Journal of Cardiovascular Translational Research, 2019
Recommended citation: Van den Broek HT, Wenker S, van de Leur RR, Doevendans PA, Chamuleau SAJ, van Slochteren FJ, van Es R. 3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging. J Cardiovasc Transl Res. 2019;12:517–527.
Incorporating repeated measurements into prediction models in the critical care setting: A framework, systematic review and meta-analysis
Published in BMC Medical Research Methodology, 2019
Recommended citation: Plate JDJ, Van De Leur RR, Leenen LPH, Hietbrink F, Peelen LM, Eijkemans MJC. Incorporating repeated measurements into prediction models in the critical care setting: A framework, systematic review and meta-analysis. BMC Med Res Methodol. 2019;19.
Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks
Published in Journal of the American Heart Association, 2020
Recommended citation: van de Leur RR, Blom LJ, Gavves E, Hof IE, van der Heijden JF, Clappers NC, Doevendans PA, Hassink RJ, van Es R. Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks. J Am Heart Assoc. 2020
fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study
Published in NeuroImage: Clinical, 2020
Recommended citation: van Montfort SJT, Slooter AJC, Kant IMJ, van de Leur RR, Spies C, de Bresser J, Witkamp TD, Hendrikse J, van Dellen E. fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study. NeuroImage Clin. 2020;27:102347.
Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology
Published in Arrhythmia & Electrophysiology Review, 2020
The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity,clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications forelectrophysiology before their clinical implementation. Read more
Recommended citation: Van de Leur RR*, Boonstra MJ*, Bagheri A, Roudijk RW, Sammani A, Taha K, Doevendans PA, Van der Harst P, Van Dam P, Hassink R, Van Es R, Asselbergs FW. Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology. Arrhythmia Electrophysiol Rev. 2020;9(3):146-54.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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