Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints

By Alessio Quaglino, Simone Pezzuto, Phaedon-Stelios Koutsourelakis, A. Auricchio, Rolf H. Krause. 2018

44 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Paris Perdikaris
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Ellen Kuhl
5. Yibo Yang

In Control and Systems Engineering

1. Yibo Yang
2. Ellen Kuhl
3. Paris Perdikaris
4. G. Em Karniadakis
5. Maizar Raissi

Citations in the corpus

• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, background): “There is a recent focus into incorporating the uncertainty associated with these maps [3, 5].”
• Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification (2019, methodology): “While multi-fidelity methods have recently been proposed for the uncertainty quantification of cardiac activation sequences [72], here, we use these techniques to characterize the effect of drugs on cardiac activation.”