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Volumn 69, Issue 21, 2017, Pages 2657-2664

Artificial Intelligence in Precision Cardiovascular Medicine

Author keywords

big data; cognitive computing; deep learning; machine learning

Indexed keywords

ANTICOAGULANT AGENT; ANTITHROMBOCYTIC AGENT; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR; ENKEPHALINASE INHIBITOR;

EID: 85019606440     PISSN: 07351097     EISSN: 15583597     Source Type: Journal    
DOI: 10.1016/j.jacc.2017.03.571     Document Type: Review
Times cited : (681)

References (31)
  • 1
    • 0030069896 scopus 로고    scopus 로고
    • Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors
    • Harrell, F.E. Jr., Lee, K.L., Mark, D.B., Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15 (1996), 361–387.
    • (1996) Stat Med , vol.15 , pp. 361-387
    • Harrell, F.E.1    Lee, K.L.2    Mark, D.B.3
  • 2
    • 84875154847 scopus 로고    scopus 로고
    • Cardiovascular disease risk assessment: insights from Framingham
    • D'Agostino, R.B. Sr., Pencina, M.J., Massaro, J.M., Coady, S., Cardiovascular disease risk assessment: insights from Framingham. Glob Heart 8 (2013), 11–23.
    • (2013) Glob Heart , vol.8 , pp. 11-23
    • D'Agostino, R.B.1    Pencina, M.J.2    Massaro, J.M.3    Coady, S.4
  • 4
    • 84964963457 scopus 로고    scopus 로고
    • Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention
    • Yeh, R.W., Secemsky, E.A., Kereiakes, D.J., et al., DAPT Study Investigators. Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention. JAMA 315 (2016), 1735–1749.
    • (2016) JAMA , vol.315 , pp. 1735-1749
    • Yeh, R.W.1    Secemsky, E.A.2    Kereiakes, D.J.3
  • 5
    • 84903165096 scopus 로고    scopus 로고
    • 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. [Published correction appears in J Am Coll Cardiol 2014;63:3026.]
    • Goff, D.C. Jr., Lloyd-Jones, D.M., Bennett, G., et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. [Published correction appears in J Am Coll Cardiol 2014;63:3026.]. J Am Coll Cardiol 63 (2014), 2935–2959.
    • (2014) J Am Coll Cardiol , vol.63 , pp. 2935-2959
    • Goff, D.C.1    Lloyd-Jones, D.M.2    Bennett, G.3
  • 6
    • 85002919424 scopus 로고    scopus 로고
    • Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update
    • Cook, N.R., Ridker, P., Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann Intern Med 165 (2016), 786–794.
    • (2016) Ann Intern Med , vol.165 , pp. 786-794
    • Cook, N.R.1    Ridker, P.2
  • 7
    • 84927626763 scopus 로고    scopus 로고
    • Phenomapping for novel classification of heart failure with preserved ejection fraction
    • Shah, S.J., Katz, D.H., Selvaraj, S., et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131 (2015), 269–279.
    • (2015) Circulation , vol.131 , pp. 269-279
    • Shah, S.J.1    Katz, D.H.2    Selvaraj, S.3
  • 8
    • 84956503197 scopus 로고    scopus 로고
    • Diagnosis of acute coronary syndrome with a support vector machine
    • Berikol, G.B., Yildiz, O., Özcan, I.T., Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst, 40, 2016, 84.
    • (2016) J Med Syst , vol.40 , pp. 84
    • Berikol, G.B.1    Yildiz, O.2    Özcan, I.T.3
  • 10
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000), 906–914.
    • (2000) Bioinformatics , vol.16 , pp. 906-914
    • Furey, T.S.1    Cristianini, N.2    Duffy, N.3    Bednarski, D.W.4    Schummer, M.5    Haussler, D.6
  • 11
    • 0034602774 scopus 로고    scopus 로고
    • Knowledge-based analysis of microarray gene expression data by using support vector machines
    • Brown, M.P., Grundy, W.N., Lin, D., et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 97 (2000), 262–267.
    • (2000) Proc Natl Acad Sci U S A , vol.97 , pp. 262-267
    • Brown, M.P.1    Grundy, W.N.2    Lin, D.3
  • 12
    • 84856495532 scopus 로고    scopus 로고
    • Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device
    • Wang, Y., Simon, M.A., Bonde, P., et al. Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device. J Heart Lung Transplant 31 (2012), 140–149.
    • (2012) J Heart Lung Transplant , vol.31 , pp. 140-149
    • Wang, Y.1    Simon, M.A.2    Bonde, P.3
  • 13
    • 85016207381 scopus 로고    scopus 로고
    • Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis
    • Motwani, M., Dey, D., Berman, D.S., et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J 38 (2017), 500–507.
    • (2017) Eur Heart J , vol.38 , pp. 500-507
    • Motwani, M.1    Dey, D.2    Berman, D.S.3
  • 15
    • 84909634152 scopus 로고    scopus 로고
    • A machine learning system to improve heart failure patient assistance
    • Guidi, G., Pettenati, M.C., Melillo, P., Iadanza, E., A machine learning system to improve heart failure patient assistance. IEEE J Biomed Health Inform 18 (2014), 1750–1756.
    • (2014) IEEE J Biomed Health Inform , vol.18 , pp. 1750-1756
    • Guidi, G.1    Pettenati, M.C.2    Melillo, P.3    Iadanza, E.4
  • 18
    • 84981161278 scopus 로고    scopus 로고
    • Detection of cardiovascular disease risk's level for adults using naive Bayes classifier
    • Miranda, E., Irwansyah, E., Amelga, A.Y., Maribondang, M.M., Salim, M., Detection of cardiovascular disease risk's level for adults using naive Bayes classifier. Healthc Inform Res 22 (2016), 196–205.
    • (2016) Healthc Inform Res , vol.22 , pp. 196-205
    • Miranda, E.1    Irwansyah, E.2    Amelga, A.Y.3    Maribondang, M.M.4    Salim, M.5
  • 19
    • 85019633510 scopus 로고    scopus 로고
    • GW27-e0397: an analysis and diagnosis system of coronary heart disease based on big data platform
    • Letian, W., Han, L., Zhang, L., Guo, S., GW27-e0397: an analysis and diagnosis system of coronary heart disease based on big data platform. J Am Coll Cardiol, 68, 2016, C82.
    • (2016) J Am Coll Cardiol , vol.68 , pp. C82
    • Letian, W.1    Han, L.2    Zhang, L.3    Guo, S.4
  • 20
    • 84867873822 scopus 로고    scopus 로고
    • Fuzzy expert system approach for coronary artery disease screening using clinical parameters
    • Pal, D., Mandana, K.M., Pal, S., Sarkar, D., Chakraborty, C., Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl-Based Syst 36 (2012), 162–174.
    • (2012) Knowl-Based Syst , vol.36 , pp. 162-174
    • Pal, D.1    Mandana, K.M.2    Pal, S.3    Sarkar, D.4    Chakraborty, C.5
  • 21
    • 84963823758 scopus 로고    scopus 로고
    • Fuzzy logic-based model to stratify cardiac surgery risk
    • Available at: Accessed March 23, 2017
    • Borracci, R.A., Arribalzaga, E.B., Fuzzy logic-based model to stratify cardiac surgery risk. Rev Argent Cardiol, 83, 2015 Available at: http://ppct.caicyt.gov.ar/index.php/rac/article/view/6730 Accessed March 23, 2017.
    • (2015) Rev Argent Cardiol , vol.83
    • Borracci, R.A.1    Arribalzaga, E.B.2
  • 22
    • 77958014769 scopus 로고    scopus 로고
    • Cardiac arrhythmia classification using fuzzy classifiers
    • Anuradha, B., Reddy, V.C.V., Cardiac arrhythmia classification using fuzzy classifiers. JATIT 4 (2008), 353–359.
    • (2008) JATIT , vol.4 , pp. 353-359
    • Anuradha, B.1    Reddy, V.C.V.2
  • 23
    • 84861810268 scopus 로고    scopus 로고
    • A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease
    • Muthukaruppan, S., Er, M.J., A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Syst Appl 39 (2012), 11657–11665.
    • (2012) Expert Syst Appl , vol.39 , pp. 11657-11665
    • Muthukaruppan, S.1    Er, M.J.2
  • 24
    • 84860260435 scopus 로고    scopus 로고
    • Detection and localization of myocardial infarction using K-nearest neighbor classifier
    • Arif, M., Malagore, I.A., Afsar, F.A., Detection and localization of myocardial infarction using K-nearest neighbor classifier. J Med Syst 36 (2012), 279–289.
    • (2012) J Med Syst , vol.36 , pp. 279-289
    • Arif, M.1    Malagore, I.A.2    Afsar, F.A.3
  • 25
    • 84884704150 scopus 로고    scopus 로고
    • QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases
    • Saini, I., Singh, D., Khosla, A., QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res 4 (2013), 331–344.
    • (2013) J Adv Res , vol.4 , pp. 331-344
    • Saini, I.1    Singh, D.2    Khosla, A.3
  • 26
    • 85019560675 scopus 로고    scopus 로고
    • Diagnosis of heart disease via CNNs (CS231n). Stanford University. Available at: Accessed March 26.
    • Wang K, Kong Y. Diagnosis of heart disease via CNNs (CS231n). Stanford University. Available at: https://www.studocu.com/en-au/document/stanford-university/convolutional-neural-networks-for-visual-recognition/practical/practical-diagnosis-of-heart-disease-via-cnns/751944/view?auth=0&auth_prem=0&new_title=0&has_flashcards=true. Accessed March 26, 2017.
    • (2017)
    • Wang, K.1    Kong, Y.2
  • 27
    • 84946734827 scopus 로고    scopus 로고
    • Deep visual-semantic alignments for generating image descriptions
    • Karpathy, A., Li, F.F., Deep visual-semantic alignments for generating image descriptions. CVPR, 2015, 3128–3137.
    • (2015) CVPR , pp. 3128-3137
    • Karpathy, A.1    Li, F.F.2
  • 28
    • 84921940378 scopus 로고    scopus 로고
    • Learning phrase representations using RNN encoder-decoder for statistical machine translation
    • Cho, K., Van Merriënboer, B., Gulcehre, C., et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv, 2014, 1406.1078.
    • (2014) arXiv , pp. 1406.1078
    • Cho, K.1    Van Merriënboer, B.2    Gulcehre, C.3
  • 29
    • 85016146323 scopus 로고    scopus 로고
    • Using recurrent neural network models for early detection of heart failure onset
    • Choi, E., Schuetz, A., Stewart, W.F., Sun, J., Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 24 (2017), 361–370.
    • (2017) J Am Med Inform Assoc , vol.24 , pp. 361-370
    • Choi, E.1    Schuetz, A.2    Stewart, W.F.3    Sun, J.4
  • 31
    • 84975795358 scopus 로고    scopus 로고
    • Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy
    • Sengupta, P.P., Huang, Y.M., Bansal, M., et al. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging, 9, 2016, e004330.
    • (2016) Circ Cardiovasc Imaging , vol.9 , pp. e004330
    • Sengupta, P.P.1    Huang, Y.M.2    Bansal, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.