State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System

Overview

Journal J Am Heart Assoc

Specialty Cardiology & Vascular Diseases

Date 2020 Feb 19

PMID 32067584

Citations 45

Authors

Rahul Kumar Sevakula

Wan-Tai M Au-Yeung

Jagmeet P Singh

E Kevin Heist

Eric M Isselbacher

Antonis A Armoundas

Affiliations

Soon will be listed here.

Citing Articles

Artificial Intelligence in Fetal and Pediatric Echocardiography.

Wang A, Doan T, Reddy C, Jone P Children (Basel). 2025; 12(1.

PMID: 39857845 PMC: 11764430. DOI: 10.3390/children12010014.

Clinically interpretable multiclass neural network for discriminating cardiac diseases.

Sbrollini A, Leoni C, Morettini M, Swenne C, Burattini L Heliyon. 2025; 11(1):e41195.

PMID: 39834449 PMC: 11742852. DOI: 10.1016/j.heliyon.2024.e41195.

Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis.

Ong S, Ahmad H PeerJ. 2024; 12:e17045.

PMID: 39670104 PMC: 11636683. DOI: 10.7717/peerj.17045.

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?.

Bota P, Thambiraj G, Bollepalli S, Armoundas A Curr Cardiol Rep. 2024; 26(12):1477-1485.

PMID: 39470943 DOI: 10.1007/s11886-024-02146-y.

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Fassina L, Lo Muzio F, Berboth L, Otvos J, Faragli A, Alogna A J Cardiovasc Transl Res. 2024; 17(6):1307-1315.

PMID: 39017912 PMC: 11634911. DOI: 10.1007/s12265-024-10546-2.

References

Hannun A, Rajpurkar P, Haghpanahi M, Tison G, Bourn C, Turakhia M . Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019; 25(1):65-69. PMC: 6784839. DOI: 10.1038/s41591-018-0268-3. View

Attia Z, Kapa S, Lopez-Jimenez F, McKie P, Ladewig D, Satam G . Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019; 25(1):70-74. DOI: 10.1038/s41591-018-0240-2. View

Sengupta P, Huang Y, Bansal M, Ashrafi A, Fisher M, Shameer K . Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging. 2016; 9(6). PMC: 5321667. DOI: 10.1161/CIRCIMAGING.115.004330. View

Madani A, Arnaout R, Mofrad M, Arnaout R . Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. 2019; 1. PMC: 6395045. DOI: 10.1038/s41746-017-0013-1. View

Burke L, Ma J, Azar K, Bennett G, Peterson E, Zheng Y . Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. Circulation. 2015; 132(12):1157-213. PMC: 7313380. DOI: 10.1161/CIR.0000000000000232. View

Khera A, Chaffin M, Aragam K, Haas M, Roselli C, Choi S . Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018; 50(9):1219-1224. PMC: 6128408. DOI: 10.1038/s41588-018-0183-z. View

Khera A, Chaffin M, Zekavat S, Collins R, Roselli C, Natarajan P . Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction. Circulation. 2018; 139(13):1593-1602. PMC: 6433484. DOI: 10.1161/CIRCULATIONAHA.118.035658. View

Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T . The rise of deep learning in drug discovery. Drug Discov Today. 2018; 23(6):1241-1250. DOI: 10.1016/j.drudis.2018.01.039. View

Zhao J, Feng Q, Wu P, Lupu R, Wilke R, Wells Q . Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Sci Rep. 2019; 9(1):717. PMC: 6345960. DOI: 10.1038/s41598-018-36745-x. View

10.

Avendi M, Kheradvar A, Jafarkhani H . A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016; 30:108-119. DOI: 10.1016/j.media.2016.01.005. View

11.

Diller G, Kempny A, Babu-Narayan S, Henrichs M, Brida M, Uebing A . Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. Eur Heart J. 2019; 40(13):1069-1077. PMC: 6441851. DOI: 10.1093/eurheartj/ehy915. View

12.

Gawehn E, Hiss J, Schneider G . Deep Learning in Drug Discovery. Mol Inform. 2016; 35(1):3-14. DOI: 10.1002/minf.201501008. View

13.

Li Q, Mark R, Clifford G . Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas. 2008; 29(1):15-32. PMC: 2259026. DOI: 10.1088/0967-3334/29/1/002. View

14.

Ortin S, Soriano M, Alfaras M, Mirasso C . Automated real-time method for ventricular heartbeat classification. Comput Methods Programs Biomed. 2019; 169:1-8. DOI: 10.1016/j.cmpb.2018.11.005. View

15.

Sevakula R, Au-Yeung W, Singh J, Heist E, Isselbacher E, Armoundas A . 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. PMC: 7070211. DOI: 10.1161/JAHA.119.013924. View

16.

Wang Z, Shah A, Tate A, Denaxas S, Shawe-Taylor J, Hemingway H . Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLoS One. 2012; 7(1):e30412. PMC: 3261909. DOI: 10.1371/journal.pone.0030412. View

17.

Peterson T, Doughty E, Kann M . Towards precision medicine: advances in computational approaches for the analysis of human variants. J Mol Biol. 2013; 425(21):4047-63. PMC: 3807015. DOI: 10.1016/j.jmb.2013.08.008. View

18.

Wang J, Yang X, Cai H, Tan W, Jin C, Li L . Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Sci Rep. 2016; 6:27327. PMC: 4895132. DOI: 10.1038/srep27327. View

19.

Asl B, Setarehdan S, Mohebbi M . Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif Intell Med. 2008; 44(1):51-64. DOI: 10.1016/j.artmed.2008.04.007. View

20.

Sodmann P, Vollmer M, Nath N, Kaderali L . A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms. Physiol Meas. 2018; 39(10):104005. DOI: 10.1088/1361-6579/aae304. View