Imperial College London
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Dr Fu Siong Ng

Faculty of Medicine, National Heart & Lung Institute

Reader in Cardiac Electrophysiology
 
 
 
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Contact

 

+44 (0)20 7594 3614f.ng Website

 
 
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Location

 

430ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sivanandarajah:2022:10.1016/j.cvdhj.2022.04.001,
author = {Sivanandarajah, P and Wu, H and Bajaj, N and Khan, S and Ng, FS},
doi = {10.1016/j.cvdhj.2022.04.001},
journal = {Cardiovascular Digital Health Journal},
pages = {136--145},
title = {Is machine learning the future for atrial fibrillation screening?},
url = {http://dx.doi.org/10.1016/j.cvdhj.2022.04.001},
volume = {3},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
AU  - Sivanandarajah,P
AU  - Wu,H
AU  - Bajaj,N
AU  - Khan,S
AU  - Ng,FS
DO  - 10.1016/j.cvdhj.2022.04.001
EP  - 145
PY  - 2022///
SN  - 2666-6936
SP  - 136
TI  - Is machine learning the future for atrial fibrillation screening?
T2  - Cardiovascular Digital Health Journal
UR  - http://dx.doi.org/10.1016/j.cvdhj.2022.04.001
UR  - https://www.sciencedirect.com/science/article/pii/S2666693622000299?via%3Dihub
UR  - http://hdl.handle.net/10044/1/97112
VL  - 3
ER  -
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