Machine learning in the detection and management of atrial fibrillation

[1] Hindricks G(2021)2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS) Eur Heart J 42 373-498

[2] Potpara T(2014)Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study Circulation 129 837-847

[3] Dagres N(2015)Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis The Lancet Neurology 14 377-387

[4] Arbelo E(2021)Predictors of atrial fibrillation development in patients with embolic stroke of undetermined source: an analysis of the RE-SPECT ESUS trial Circulation 144 1738-1746

[5] Bax JJ(2021)Subclinical atrial fibrillation: a silent threat with uncertain implications Annu Rev Med 73 355-117

[6] Blomström-Lundqvist C(2015)Deep learning in neural networks: an overview Neural Netw 61 85-243

[7] Chugh SS(2020)Application of the residue number system to reduce hardware costs of the convolutional neural network implementation Math Comput Simul 177 232-55

[8] Havmoeller R(2020)An introductory review of deep learning for prediction models with big data Front Artif Intell 3 4-449

[9] Narayanan K(2022)A guide to machine learning for biologists Nat Rev Mol Cell Biol 23 40-444

[10] Singh D(2020)Artificial intelligence in medicine: chances and challenges for wide clinical adoption Visc Med 36 443-39

[1] Hindricks G(2021)2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS) Eur Heart J 42 373-498

[2] Potpara T(2014)Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study Circulation 129 837-847

[3] Dagres N(2015)Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis The Lancet Neurology 14 377-387

[4] Arbelo E(2021)Predictors of atrial fibrillation development in patients with embolic stroke of undetermined source: an analysis of the RE-SPECT ESUS trial Circulation 144 1738-1746

[5] Bax JJ(2021)Subclinical atrial fibrillation: a silent threat with uncertain implications Annu Rev Med 73 355-117

[6] Blomström-Lundqvist C(2015)Deep learning in neural networks: an overview Neural Netw 61 85-243

[7] Chugh SS(2020)Application of the residue number system to reduce hardware costs of the convolutional neural network implementation Math Comput Simul 177 232-55

[8] Havmoeller R(2020)An introductory review of deep learning for prediction models with big data Front Artif Intell 3 4-449

[9] Narayanan K(2022)A guide to machine learning for biologists Nat Rev Mol Cell Biol 23 40-444

[10] Singh D(2020)Artificial intelligence in medicine: chances and challenges for wide clinical adoption Visc Med 36 443-39