Holkeri A, Eranti A, Haukilahti MAE, Kerola T, Kentta TV, Tikkanen JT, et al. Predicting sudden cardiac death in a general population using an electrocardiographic risk score. Heart. 2020;106(6):427–33.
Article
Google Scholar
Oliver JM, Gallego P, Gonzalez AE, Avila P, Alonso A, Garcia-Hamilton D, et al. Predicting sudden cardiac death in adults with congenital heart disease. Heart. 2021;107(1):67–75.
Article
Google Scholar
Lee S, Wong WT, Wong ICK, Mak C, Mok NS, Liu T, et al. Ventricular Tachyarrhythmia risk in paediatric/young vs. adult brugada syndrome patients: a territory-wide study. Front Cardiovasc Med. 2021;8:671666.
Kayvanpour E, Sammani A, Sedaghat-Hamedani F, Lehmann DH, Broezel A, Koelemenoglu J, et al. A novel risk model for predicting potentially life-threatening arrhythmias in non-ischemic dilated cardiomyopathy (DCM-SVA risk). Int J Cardiol. 2021.
Maupain C, Badenco N, Pousset F, Waintraub X, Duthoit G, Chastre T, et al. Risk stratification in arrhythmogenic right ventricular cardiomyopathy/dysplasia without an implantable cardioverter-defibrillator. JACC Clin Electrophysiol. 2018;4(6):757–68.
Article
Google Scholar
Ostman-Smith I, Sjoberg G, Alenius Dahlqvist J, Larsson P, Fernlund E. Sudden cardiac death in childhood hypertrophic cardiomyopathy is best predicted by a combination of ECG risk-score and HCMRisk-Kids score. Acta Paediatr. 2021.
Mohd Faizal AS, Thevarajah TM, Khor SM, Chang SW. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput Methods Programs Biomed. 2021;207:106190.
Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156–64.
Article
Google Scholar
Au-Yeung WM, Sahani AK, Isselbacher EM, Armoundas AA. Reduction of false alarms in the intensive care unit using an optimized machine learning based approach. NPJ Digit Med. 2019;2:86.
Article
Google Scholar
Bollepalli SC, Sevakula RK, Au-Yeung WM, Kassab MB, Merchant FM, Bazoukis G, et al. Real-time arrhythmia detection using hybrid convolutional neural networks. JAHA (in press).
Sevakula RK, Au-Yeung WM, Singh JP, Heist EK, Isselbacher EM, Armoundas AA. State-of-the-art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system. J Am Heart Assoc. 2020;9(4):e013924.
Bazoukis G, Stavrakis S, Zhou J, Bollepalli SC, Tse G, Zhang Q, et al. Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review. Heart Fail Rev. 2021;26(1):23–34.
Article
Google Scholar
Bazoukis G, Hall J, Loscalzo J, Antman E, Fuster V, Armoundas AA. The augmented intelligence in medicine: a framework for successful implementation. Cell Rep Med. (in press).
Saad Albawi OB, Saad Al-Azawi & Osman N. Ucan understanding of a convolutional neural network. IEEE. 2017.
Hu W, Hsieh MH, Lin CL. A novel atrial fibrillation prediction model for Chinese subjects: a nationwide cohort investigation of 682 237 study participants with random forest model. EP Europace. 2019;21(9):1307–12.
Article
Google Scholar
Erdenebayar U, Kim H, Park JU, Kang D, Lee KJ. Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal. J Korean Med Sci. 2019;34(7):e64.
Gao J, Zhang H, Lu P, Wang Z. An effective LSTM recurrent network to detect arrhythmia on imbalanced ECG dataset. J Healthc Eng. 2019.
Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, et al. 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation. 2020;142(25):e533–57.
PubMed
Google Scholar
Bhattacharya M, Lu DY, Kudchadkar SM, Greenland GV, Lingamaneni P, Corona-Villalobos CP, et al. Identifying ventricular arrhythmias and their predictors by applying machine learning methods to electronic health records in patients with hypertrophic cardiomyopathy (HCM-VAr-Risk Model). Am J Cardiol. 2019;123(10):1681–9.
Article
Google Scholar
Smole T, Zunkovic B, Piculin M, Kokalj E, Robnik-Sikonja M, Kukar M, et al. A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. Comput Biol Med. 2021;135:104648.
Rowin EJ, Maron MS. The role of cardiac MRI in the diagnosis and risk stratification of hypertrophic cardiomyopathy. Arrhythm Electrophysiol Rev. 2016;5(3):197–202.
Article
Google Scholar
Kamp NJ, Chery G, Kosinski AS, Desai MY, Wazni O, Schmidler GS, et al. Risk stratification using late gadolinium enhancement on cardiac magnetic resonance imaging in patients with hypertrophic cardiomyopathy: a systematic review and meta-analysis. Prog Cardiovasc Dis. 2021;66:10–6.
Article
Google Scholar
Freitas P, Ferreira AM, Arteaga-Fernández E, de Oliveira AM, Mesquita J, Abecasis J, et al. The amount of late gadolinium enhancement outperforms current guideline-recommended criteria in the identification of patients with hypertrophic cardiomyopathy at risk of sudden cardiac death. J Cardiovasc Magn Reson. 2019;21(1):50.
Article
Google Scholar
Alis D, Guler A, Yergin M, Asmakutlu O. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI. Diagn Interv Imaging. 2020;101(3):137–46.
Article
CAS
Google Scholar
Lyon A, Ariga R, Minchole A, Mahmod M, Ormondroyd E, Laguna P, et al. Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers. Front Physiol. 2018;9:213.
Article
Google Scholar
Yalin K, Golcuk E, Aksu T. Cardiac magnetic resonance for ventricular arrhythmia therapies in patients with coronary artery disease. J Atr Fibrillation. 2015;8(1):1242.
PubMed
PubMed Central
Google Scholar
Kotu LP, Engan K, Borhani R, Katsaggelos AK, Orn S, Woie L, et al. Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artif Intell Med. 2015;64(3):205–15.
Article
Google Scholar
Okada DR, Miller J, Chrispin J, Prakosa A, Trayanova N, Jones S, et al. Substrate spatial complexity analysis for the prediction of ventricular arrhythmias in patients with ischemic cardiomyopathy. Circ Arrhythm Electrophysiol. 2020;13(4):e007975.
Rogers AJ, Selvalingam A, Alhusseini MI, Krummen DE, Corrado C, Abuzaid F, et al. Machine learned cellular phenotypes in cardiomyopathy predict sudden death. Circ Res. 2021;128(2):172–84.
Article
CAS
Google Scholar
Bardy GH, Lee KL, Mark DB, Poole JE, Packer DL, Boineau R, et al. Amiodarone or an implantable cardioverter-defibrillator for congestive heart failure. N Engl J Med. 2005;352(3):225–37.
Article
CAS
Google Scholar
Kober L, Thune JJ, Nielsen JC, Haarbo J, Videbaek L, Korup E, et al. Defibrillator implantation in patients with nonischemic systolic heart failure. N Engl J Med. 2016;375(13):1221–30.
Article
Google Scholar
Meng F, Zhang Z, Hou X, Qian Z, Wang Y, Chen Y, et al. Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China. BMJ Open. 2019;9(5):e023724.
Wu KC, Wongvibulsin S, Tao S, Ashikaga H, Stillabower M, Dickfeld TM, et al. Baseline and dynamic risk predictors of appropriate implantable cardioverter defibrillator therapy. J Am Heart Assoc. 2020;9(20):e017002.
Bazoukis G, Tse G, Korantzopoulos P, Liu T, Letsas KP, Stavrakis S, et al. Impact of implantable cardioverter-defibrillator interventions on all-cause mortality in heart failure patients: a meta-analysis. Cardiol Rev. 2019;27(3):160–6.
Article
Google Scholar
Shakibfar S, Krause O, Lund-Andersen C, Aranda A, Moll J, Andersen TO, et al. Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning. Europace. 2019;21(2):268–74.
Article
Google Scholar
Ramchand J, Podugu P, Obuchowski N, Harb SC, Chetrit M, Milinovich A, et al. Novel approach to risk stratification in left ventricular non-compaction using a combined cardiac imaging and plasma biomarker approach. J Am Heart Assoc. 2021;10(8):e019209.
Rocon C, Tabassian M, Tavares de Melo MD, de Araujo Filho JA, Grupi CJ, Parga Filho JR, et al. Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study. ESC Heart Fail. 2020;7(5):2431–9.
Al-Khatib SM, Stevenson WG, Ackerman MJ, Bryant WJ, Callans DJ, Curtis AB, et al. 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. Circulation. 2018;138(13):e210–71.
PubMed
Google Scholar
Ohira H, Ardle BM, deKemp RA, Nery P, Juneau D, Renaud JM, et al. Inter- and intraobserver agreement of (18)F-FDG PET/CT image interpretation in patients referred for assessment of cardiac sarcoidosis. J Nucl Med. 2017;58(8):1324–9.
Article
CAS
Google Scholar
Togo R, Hirata K, Manabe O, Ohira H, Tsujino I, Magota K, et al. Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Comput Biol Med. 2019;104:81–6.
Article
Google Scholar
Coleman GC, Shaw PW, Balfour PC Jr, Gonzalez JA, Kramer CM, Patel AR, et al. Prognostic value of myocardial scarring on CMR in patients with cardiac sarcoidosis. JACC Cardiovasc Imaging. 2017;10(4):411–20.
Article
Google Scholar
Okada DR, Xie E, Assis F, Smith J, Derakhshan A, Gowani Z, et al. Regional abnormalities on cardiac magnetic resonance imaging and arrhythmic events in patients with cardiac sarcoidosis. J Cardiovasc Electrophysiol. 2019;30(10):1967–76.
Article
Google Scholar
Lee S, Zhou J, Li KHC, Leung KSK, Lakhani I, Liu T, et al. Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation. Open Heart. 2021;8(1).
Tse G, Lee S, Zhou J, Liu T, Wong ICK, Mak C, et al. Territory-wide Chinese cohort of long QT syndrome: random survival forest and cox analyses. Front Cardiovasc Med. 2021;8:608592.
Clerx M, Heijman J, Collins P, Volders PGA. Predicting changes to INa from missense mutations in human SCN5A. Sci Rep. 2018;8(1):12797.
Article
Google Scholar
Lee S, Zhou J, Jeevaratnam K, Lakhani I, Wong WT, Wong IC, et al. Arrhythmic outcomes in catecholaminergic polymorphic ventricular tachycardia. medRxiv. 2021:2021.01.04.21249214.
Bergau DM, Liu C, Magin RL, Lu H. Machine-learning prediction of drug-induced cardiac arrhythmia: analysis of gene expression and clustering. Crit Rev Biomed Eng. 2018;46(3):245–75.
Article
Google Scholar
Yap CW, Cai CZ, Xue Y, Chen YZ. Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol Sci. 2004;79(1):170–7.
Article
CAS
Google Scholar
Sessa M, Mascolo A, Dalhoff KP, Andersen M. The risk of fractures, acute myocardial infarction, atrial fibrillation and ventricular arrhythmia in geriatric patients exposed to promethazine. Exp Opin Drug Saf. 2020;19(3):349–57.
Article
CAS
Google Scholar
Costabal FS, Matsuno K, Yao J, Perdikaris P, Kuhl E. Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Comput Methods Appl Mech Eng. 2019;348:313–33.
Article
Google Scholar
Diller GP, Orwat S, Vahle J, Bauer UMM, Urban A, Sarikouch S, et al. Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis. Heart. 2020;106(13):1007–14.
Article
CAS
Google Scholar
Sun H, Liu Y, Song B, Cui X, Luo G, Pan S. Prediction of arrhythmia after intervention in children with atrial septal defect based on random forest. BMC Pediatr. 2021;21(1):280.
Article
CAS
Google Scholar
Guo K, Fu X, Zhang H, Wang M, Hong S, Ma S. Predicting the postoperative blood coagulation state of children with congenital heart disease by machine learning based on real-world data. Transl Pediatr. 2021;10(1):33.
Article
Google Scholar
Wu X, Yuan X, Wang W, Liu K, Qin Y, Sun X, et al. Value of a machine learning approach for predicting clinical outcomes in young patients with hypertension. Hypertension. 2020;75(5):1271–8.
Article
CAS
Google Scholar
Lee H, Shin SY, Seo M, Nam GB, Joo S. Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks. Sci Rep. 2016;6:32390.
Article
CAS
Google Scholar
Chen Z, Ono N, Chen W, Tamura T, Altaf-Ul-Amin MD, Kanaya S, et al. The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals. Comput Methods Programs Biomed. 2021;205:106102.
Taye GT, Hwang HJ, Lim KM. Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features. Sci Rep. 2020;10(1):6769.
Article
CAS
Google Scholar
Taye GT, Shim EB, Hwang HJ, Lim KM. Machine learning approach to predict ventricular fibrillation based on QRS complex shape. Front Physiol. 2019;10:1193.
Article
Google Scholar
Panda R, Jain S, Tripathy RK, Acharya UR. Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Comput Biol Med. 2020;124:103939.
Picon A, Irusta U, Alvarez-Gila A, Aramendi E, Alonso-Atienza F, Figuera C, et al. Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia. PLoS ONE. 2019;14(5):e0216756.
Au-Yeung WM, Sevakula RK, Sahani AK, Kassab M, Boyer R, Isselbacher EM, et al. Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm. Eur Heart J Digit Health. 2021;2(3):437–45.
Article
Google Scholar
Christopoulos G, Graff-Radford J, Lopez CL, Yao X, Attia ZI, Rabinstein AA, et al. Artificial intelligence-electrocardiography to predict incident atrial fibrillation: a population-based study. Circ Arrhythm Electrophysiol. 2020;13(12):e009355.
Bashar SK, Ding EY, Walkey AJ, McManus DD, Chon KH. Atrial fibrillation prediction from critically ill sepsis patients. Biosensors (Basel). 2021;11(8).
Luongo G, Azzolin L, Schuler S, Rivolta MW, Almeida TP, Martinez JP, et al. Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG. Cardiovasc Digit Health J. 2021;2(2):126–36.
Article
Google Scholar
Saiz-Vivo J, Corino VDA, Hatala R, de Melis M, Mainardi LT. Heart rate variability and clinical features as predictors of atrial fibrillation recurrence after catheter ablation: a pilot study. Front Physiol. 2021;12:672896.
Ren Q, Cheng, H., & Han, H. Research on machine learning framework based on random forest algorithm. In: AIP conference proceedings. 2017;1820(1).
Au-Yeung W, Reinhall PG, Bardy GH, Brunton SL. Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data. PLoS ONE. 2018;13(11):e0207215.
Marzec L, Raghavan S, Banaei-Kashani F, Creasy S, Melanson EL, Lange L, et al. Device-measured physical activity data for classification of patients with ventricular arrhythmia events: a pilot investigation. PLoS ONE. 2018;13(10):e0206153.