Published on in Vol 8, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26964, first published .
Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Stina Matthiesen 1, 2, PhD;  Søren Zöga Diederichsen 2, 3, MD;  Mikkel Klitzing Hartmann Hansen 2, PhD;  Christina Villumsen 2, MSc;  Mats Christian Højbjerg Lassen 2, BSc;  Peter Karl Jacobsen 3, DMSc, MD;  Niels Risum 3, MD, PhD;  Bo Gregers Winkel 3, MD, PhD;  Berit T Philbert 3, MD, PhD;  Jesper Hastrup Svendsen 3, 4, DMSc, MD;  Tariq Osman Andersen 1, 2, PhD

1 Department of Computer Science, Faculty of Science, University of Copenhagen, Copenhagen , DK

2 Vital Beats , Copenhagen , DK

3 Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen , DK

4 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen , DK

Corresponding Author:

  • Stina Matthiesen, PhD
  • Department of Computer Science
  • Faculty of Science
  • University of Copenhagen
  • Universitetsparken 5
  • Copenhagen
  • DK
  • Phone: 45 21231008
  • Email: matthiesen@di.ku.dk