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Pregled bibliografske jedinice broj: 1197221

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology

Feeny, Albert K.; Chung, Mina K.; Madabhushi, Anant; Attia, Zachi I.; Cikes, Maja; Firouznia, Marjan; Friedman, Paul A.; Kalscheur, Matthew M.; Kapa, Suraj; Narayan, Sanjiv M. et al.
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology // Circulation-Arrhythmia and Electrophysiology, 13 (2020), 8; 873-890 doi:10.1161/circep.119.007952 (međunarodna recenzija, pregledni rad, stručni)

CROSBI ID: 1197221 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology

Autori
Feeny, Albert K. ; Chung, Mina K. ; Madabhushi, Anant ; Attia, Zachi I. ; Cikes, Maja ; Firouznia, Marjan ; Friedman, Paul A. ; Kalscheur, Matthew M. ; Kapa, Suraj ; Narayan, Sanjiv M. ; Noseworthy, Peter A. ; Passman, Rod S. ; Perez, Marco V. ; Peters, Nicholas S. ; Piccini, Jonathan P. ; Tarakji, Khaldoun G. ; Thomas, Suma A. ; Trayanova, Natalia A. ; Turakhia, Mintu P. ; Wang, Paul J.

Izvornik
Circulation-Arrhythmia and Electrophysiology (1941-3149) 13 (2020), 8; 873-890

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, pregledni rad, stručni

Ključne riječi
artificial intelligence ; atrial fibrillation ; cardiac electrophysiology ; computers ; diagnosis ; machine learning

Sažetak
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti



POVEZANOST RADA

Ustanove:
Medicinski fakultet, Zagreb

Profili:

Avatar Url Maja Čikeš (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Feeny, Albert K.; Chung, Mina K.; Madabhushi, Anant; Attia, Zachi I.; Cikes, Maja; Firouznia, Marjan; Friedman, Paul A.; Kalscheur, Matthew M.; Kapa, Suraj; Narayan, Sanjiv M. et al.
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology // Circulation-Arrhythmia and Electrophysiology, 13 (2020), 8; 873-890 doi:10.1161/circep.119.007952 (međunarodna recenzija, pregledni rad, stručni)
Feeny, A., Chung, M., Madabhushi, A., Attia, Z., Cikes, M., Firouznia, M., Friedman, P., Kalscheur, M., Kapa, S. & Narayan, S. (2020) Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circulation-Arrhythmia and Electrophysiology, 13 (8), 873-890 doi:10.1161/circep.119.007952.
@article{article, author = {Feeny, Albert K. and Chung, Mina K. and Madabhushi, Anant and Attia, Zachi I. and Cikes, Maja and Firouznia, Marjan and Friedman, Paul A. and Kalscheur, Matthew M. and Kapa, Suraj and Narayan, Sanjiv M. and Noseworthy, Peter A. and Passman, Rod S. and Perez, Marco V. and Peters, Nicholas S. and Piccini, Jonathan P. and Tarakji, Khaldoun G. and Thomas, Suma A. and Trayanova, Natalia A. and Turakhia, Mintu P. and Wang, Paul J.}, year = {2020}, pages = {873-890}, DOI = {10.1161/circep.119.007952}, keywords = {artificial intelligence, atrial fibrillation, cardiac electrophysiology, computers, diagnosis, machine learning}, journal = {Circulation-Arrhythmia and Electrophysiology}, doi = {10.1161/circep.119.007952}, volume = {13}, number = {8}, issn = {1941-3149}, title = {Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology}, keyword = {artificial intelligence, atrial fibrillation, cardiac electrophysiology, computers, diagnosis, machine learning} }
@article{article, author = {Feeny, Albert K. and Chung, Mina K. and Madabhushi, Anant and Attia, Zachi I. and Cikes, Maja and Firouznia, Marjan and Friedman, Paul A. and Kalscheur, Matthew M. and Kapa, Suraj and Narayan, Sanjiv M. and Noseworthy, Peter A. and Passman, Rod S. and Perez, Marco V. and Peters, Nicholas S. and Piccini, Jonathan P. and Tarakji, Khaldoun G. and Thomas, Suma A. and Trayanova, Natalia A. and Turakhia, Mintu P. and Wang, Paul J.}, year = {2020}, pages = {873-890}, DOI = {10.1161/circep.119.007952}, keywords = {artificial intelligence, atrial fibrillation, cardiac electrophysiology, computers, diagnosis, machine learning}, journal = {Circulation-Arrhythmia and Electrophysiology}, doi = {10.1161/circep.119.007952}, volume = {13}, number = {8}, issn = {1941-3149}, title = {Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology}, keyword = {artificial intelligence, atrial fibrillation, cardiac electrophysiology, computers, diagnosis, machine learning} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE

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