ABC | Volume 114, Nº4, Abril 2020

Artigo de Revisão Marques et al. Inteligência Artificial em Cardiologia Arq Bras Cardiol. 2020; 114(4):718-725 24. Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/ CT by machine learning: Anatomic and clinical validation. J Nucl Med. 2016;58(6):961-7. 25. ParedesS, RochaT , deCarvalhoP , Henriques J, Morais J, Ferreira J. Integration of different risk assessment tools to improve stratification of patients with coronary artery disease. Med Biol Eng Comput. 2015;53(10):1069-83. 26. Al-MallahMH, Elshawi R, Ahmed AM, Qureshi WT , Brawner CA , BlahaMJ, et al. Using machine learning to the association between cardiorespiratory fitness and all-causemortality (from theHenry Ford Exercise Testing Project). Am J Cardiol. 2017;120(11):2078-84. 27. Smisek R, Hejc J, Ronzhina M, Nemcova A, Marsanova L, Kolarova J, et al. Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device. Physiol Meas. 2018;39(9):094003. 28. Stuckey TD, Gammon RS, Goswami R, Depta JP, Steuter JA, Meine FJ 3rd, et al. Cardiac phase space tomography: a novel method of assessing coronary artery disease utilizingmachine learning. PLoSOne. 2018;13(8):e0198603. 29. Samad MD, Ulloa A, Wehner GJ, Jing L, Hartzel D, Good CW , et al. Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning. JACC Cardiovasc Imaging. 2019;12(4):681-9. 30. Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction bymachine learning: themulti-ethnic study of atherosclerosis. Circ Res.2017;121(9):1092-101. 31. IshwaranH, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841-60. 32. CikesM,Sanchez-MartinezS,ClaggettB,DuchateauN,PiellaG,ButakoffC,et al.Machinelearning-basedphenogroupinginheartfailuretoidentifyresponders to cardiac resynchronization therapy. Eur J Heart Fail. 2019;21(1):74-85. 33. Kwon JM, Lee Y, Lee Y , Lee S, Park J. An algorithm based on deep learning for predictingin-hospitalcardiacarrest.AmHeartAssoc. 2018;7(13):pii:e008678. 34. Rubin J, Parvaneh S, Rahman A, Conroy B, Babaeizadeh S. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. J Electrocardiol. 2018;51(6S):S18-S21. 35. Zhang J , Gajjala S, Agrawal P , Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138(16):1623-35. 36. Nakajima K, Kudo T, Nakata T, Kiso K, Kasai T, Taniguchi Y, et al. Diagnostic accuracyofanartificialneuralnetworkcomparedwithstatisticalquantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imaging. 2017;44(13):2280-9. 37. Mortazavi BJ , Bucholz EM, Desai NR, Huang C, Curtis JP , Masoudi FA, et al. Comparison of machine learningmethods with national cardiovascular data registrymodels for prediction of risk of bleeding after percutaneous coronary intervention. JAMA NetwOpen. 2019;2(7):e196835. 38. Hernesniemi JA, Mahdiani S, Tynkkynen JA, Lyytikäinen LP , Mishra PP , Lehtimäki T, et al. Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study. Ann Med. 2019;51(2):156-63. 39. NannenV. The Paradox of Overfitting Volker Nannen. [thesis] . Países Baixos: Faculty of Artificial Intelligence at the University of Groningen; 2003. 40. Cortes C,VapnikV.Support-vector networks.MachLearn.1995;20:273-97. 41. Bayes T. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated byMr. Price, in a letter to John Canton, A. M. F. R. S. Philos Trans R Soc Lond. 1763;53:370-418. 42. Webb GI, Boughton JR, Wang Z. Not so naive bayes: aggregating one- dependence estimators. Mach Learn. 2005;58(1):5-24. 43. Watkins, DS. Fundamentals of matrix computations. 2th ed. New York: Wiley-Interscience; 2002. 44. Cover T , Hart P . (1967). Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21-7. 45. Fix, E., Hodges, J.L. Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field, Texas, 1951. 46. Holland JH. Adaptation in natural and artificial systems. 2th ed. Cambridge, MA: MIT Press; 1992. 47. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32. 48. Ho TK. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition; 1995 Aug 14-16; Montreal. Washington, DC: IEEE Computer Society; 1995. p.278-82. 49. MacQueen JB. SomeMethods for classification and Analysis of Multivariate Observations. Proc. Fifth Berkeley Symp. on Math. Statist. and Prob. 1967;1:281-97. 50. McCulloch WS, Pitts W. A logical calculus of ideas immanent in nervous activity. Bull Math Biophys. 1943;5(4):115-33. 51. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386-408. 52. BroomheadDS,LoweD. Multivariable functional interpolationandadaptive networks. Complex Syst. 1988;2:321-55. 53. Friedman JH. Greedy function approximation: a gradient boostingmachine. Ann Stat. 2001;29(5):1189-232. 54. Friedman JH. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002;38(4):367-78. 55. Wang Y, Kosinski M. Deep neural networks aremore accurate than humans at detecting sexual orientation from facial images. J Pers Soc Psychol. 2018;114(2):246-57. 56. Ma H, Marti-Gutierrez N, Park SW , Wu J, Lee Y , Suzuki K, et al. Correction of a pathogenicgenemutationinhumanembryos .Nature. 2017;548(7668):413-9. 57. Shah SJ, Katz DH, Deo RC. Phenotypic spectrum of heart failure with preserved ejection fraction. Heart Fail Clin. 2014;10(3):407-18. 58. Medina EL, Mesquita CT, Loques Filho O. Healthcare social networks for patients with cardiovascular diseases and recommendation systems. Int J Cardiovasc Sci. 2016;29(1):80-5. 59. Bittencourt MS. From evidence-based medicine to precision health: using data to personalize care. Arq Bras Cardiol. 2018;111(6):762-3. Este é um artigo de acesso aberto distribuído sob os termos da licença de atribuição pelo Creative Commons 725