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, Denmark

2 Vital Beats, Copenhagen, Denmark

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

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

Corresponding Author:

  • Stina Matthiesen, PhD
  • Department of Computer Science
  • Faculty of Science
  • University of Copenhagen
  • Universitetsparken 5
  • Copenhagen, 2100
  • Denmark
  • Phone: 45 21231008
  • Email: matthiesen@di.ku.dk