Artificial Intelligence in Precision Cardiovascular Medicine

Journal of the American College of Cardiology - Tập 69 - Trang 2657-2664 - 2017
Chayakrit Krittanawong1,2, HongJu Zhang3, Zhen Wang4,5, Mehmet Aydar2,6, Takeshi Kitai2,7
1Department of Internal Medicine, Icahn School of Medicine at Mount Sinai St. Luke's and Mount Sinai West, New York, New York
2Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio
3Division of Cardiovascular Disease, Department of Medicine, Mayo Clinic, Rochester, Minnesota
4Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
5Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
6Department of Computer Science at Kent State University, Kent, Ohio
7Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan

Tài liệu tham khảo

Harrell, 1996, Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Stat Med, 15, 361, 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 D'Agostino, 2013, Cardiovascular disease risk assessment: insights from Framingham, Glob Heart, 8, 11, 10.1016/j.gheart.2013.01.001 van den Ham, 2015, Comparative performance of ATRIA, CHADS2, and CHA2DS2-VASc risk scores predicting stroke in patients with atrial fibrillation: results from a national primary care database, J Am Coll Cardiol, 66, 1851, 10.1016/j.jacc.2015.08.033 Yeh, 2016, Development and validation of a prediction rule for benefit and harm of dual antiplatelet therapy beyond 1 year after percutaneous coronary intervention, JAMA, 315, 1735, 10.1001/jama.2016.3775 Goff, 2014, 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, 2935, 10.1016/j.jacc.2013.11.005 Cook, 2016, Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update, Ann Intern Med, 165, 786, 10.7326/M16-1739 Shah, 2015, Phenomapping for novel classification of heart failure with preserved ejection fraction, Circulation, 131, 269, 10.1161/CIRCULATIONAHA.114.010637 Berikol, 2016, Diagnosis of acute coronary syndrome with a support vector machine, J Med Syst, 40, 84 Balasubramanian V, Gouripeddi R, Panchanathan S, Vermillion J, Bhaskaran A, Siegel R. Support vector machine based conformal predictors for risk of complications following a coronary drug eluting stent procedure. 2009 36th Annual Computers in Cardiology Conference (CinC), Park City, UT; 2009:5–8. Furey, 2000, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906, 10.1093/bioinformatics/16.10.906 Brown, 2000, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc Natl Acad Sci U S A, 97, 262, 10.1073/pnas.97.1.262 Wang, 2012, Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device, J Heart Lung Transplant, 31, 140, 10.1016/j.healun.2011.11.003 Motwani, 2017, 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, 500 Panahiazar, 2015, Using EHRs and machine learning for heart failure survival analysis, Stud Health Technol Inform, 216, 40 Guidi, 2014, A machine learning system to improve heart failure patient assistance, IEEE J Biomed Health Inform, 18, 1750, 10.1109/JBHI.2014.2337752 Ishwaran, 2008, Random survival forests, Ann Appl Stat, 2, 841, 10.1214/08-AOAS169 Zolfaghar K, Meadem N, Teredesai A, Roy SB, Chin SC, Muckian B. Big data solutions for predicting risk-of-readmission for congestive heart failure patients. 2013 IEEE International Conference on Big Data, Silicon Valley, CA; 2013:64–71. Miranda, 2016, Detection of cardiovascular disease risk's level for adults using naive Bayes classifier, Healthc Inform Res, 22, 196, 10.4258/hir.2016.22.3.196 Letian, 2016, GW27-e0397: an analysis and diagnosis system of coronary heart disease based on big data platform, J Am Coll Cardiol, 68, C82, 10.1016/j.jacc.2016.07.308 Pal, 2012, Fuzzy expert system approach for coronary artery disease screening using clinical parameters, Knowl-Based Syst, 36, 162, 10.1016/j.knosys.2012.06.013 Borracci, 2015, Fuzzy logic-based model to stratify cardiac surgery risk, Rev Argent Cardiol, 83, 10.7775/rac.v83.i4.6730 Anuradha, 2008, Cardiac arrhythmia classification using fuzzy classifiers, JATIT, 4, 353 Muthukaruppan, 2012, A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease, Expert Syst Appl, 39, 11657, 10.1016/j.eswa.2012.04.036 Arif, 2012, Detection and localization of myocardial infarction using K-nearest neighbor classifier, J Med Syst, 36, 279 Saini, 2013, QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases, J Adv Res, 4, 331 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. Karpathy, 2015, Deep visual-semantic alignments for generating image descriptions, CVPR, 3128 Cho, 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv, 1406.1078 Choi, 2017, Using recurrent neural network models for early detection of heart failure onset, J Am Med Inform Assoc, 24, 361, 10.1093/jamia/ocw112 Kannathal, 2003, Classification of cardiac patient states using artificial neural networks, Exp Clin Cardiol, 8, 206 Sengupta, 2016, Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy, Circ Cardiovasc Imaging, 9, e004330, 10.1161/CIRCIMAGING.115.004330