Machine learning in cardiovascular medicine: Are we there yet?

Heart

Volumn 104, Issue 14, 2018, Pages 1156-1164

  Shameer, Khader ^a,b,c   Johnson, Kipp W ^b,c   Glicksberg, Benjamin S ^b,c,d   Dudley, Joel T ^b,c   Sengupta, Partho P ^e  

^a Northwell Health   (United States)

^b MOUNT SINAI HEALTH SYSTEM   (United States)

^c MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e WEST VIRGINIA UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; BIG DATA; CARDIAC IMAGING; CARDIOLOGY; CLASSIFIER; CLINICAL PRACTICE; DIAGNOSTIC ACCURACY; HEALTH CARE DELIVERY; HIGH THROUGHPUT SCREENING; HUMAN; LEARNING ALGORITHM; MACHINE LEARNING; NONHUMAN; PHENOTYPE; POINT OF CARE SYSTEM; PRIORITY JOURNAL; REVIEW; SENSITIVITY AND SPECIFICITY; STAKEHOLDER ENGAGEMENT; STANDARDIZATION; SUPERVISED MACHINE LEARNING; UNSUPERVISED MACHINE LEARNING; CAUSALITY; CLINICAL TRIAL (TOPIC); DIAGNOSTIC IMAGING; GENETIC PREDISPOSITION; GENOMICS; INFORMATION PROCESSING; REGISTER;

BIG DATA; CARDIOLOGY; CAUSALITY; CLINICAL TRIALS AS TOPIC; DATASETS AS TOPIC; DIAGNOSTIC IMAGING; GENETIC PREDISPOSITION TO DISEASE; GENOMICS; HUMANS; MACHINE LEARNING; PHENOTYPE; REGISTRIES;

EID: 85047537837     PISSN: 13556037     EISSN: 1468201X     Source Type: Journal    
DOI: 10.1136/heartjnl-2017-311198     Document Type: Review

References (49)

1. Johnson KW, Shameer K, Glicksberg BS, et al. Enabling precision cardiology through multiscale biology and systems medicine. JACC Basic Transl Sci 2017;2:311-27.
2. Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med 2017;376:2507-9.
3. Tajik AJ. Machine learning for echocardiographic imaging: Embarking on another incredible journey. J Am Coll Cardiol 2016;68:2296-8.
4. Nagueh SF. Unleashing the potential of machine-based learning for the diagnosis of cardiac diseases. Circ Cardiovasc Imaging 2016;9:e005059.
5. Kini AS, Vengrenyuk Y, Shameer K, et al. Intracoronary imaging, cholesterol efflux, and transcriptomes after intensive statin treatment: The YELLOW II study. J Am Coll Cardiol 2017;69:628-40.
6. Slomka PJ, Dey D, Sitek A, et al. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices 2017;14:197-212.
7. Narula S, Shameer K, Salem Omar AM, et al. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 2016;68:2287-95.
8. Sengupta PP, Huang YM, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging: A pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging 2016;9.
9. Shameer K, Johnson KW, Yahi A, et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: A case-study using mount sinai heart failure cohort. Pac Symp Biocomput 2016;22:276-87.
10. Shameer K, Tripathi LP, Kalari KR, et al. Interpreting functional effects of coding variants: Challenges in proteome-scale prediction, annotation and assessment. Brief Bioinform 2016;17:841-62.
11. Deloukas P, Kanoni S, Willenborg C, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 2013;45:25-33.
12. Shameer K, Denny JC, Ding K, et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet 2014;133:95-109.
13. Kullo IJ, Jouni H, Austin EE, et al. Incorporating a genetic risk score into coronary heart disease risk estimates: Effect on low-density lipoprotein cholesterol levels. Circulation 2016;133:1181-8.
14. Shameer K, Glicksberg BS, Hodos R, et al. Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform 2017.
15. Li L, Cheng WY, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 2015;7:311ra174.
16. Douglas PS, Garcia MJ, Haines DE, et al. ACCF/ASE/AHA/ASNC/HFSA/HRS/SCAI/ SCCM/SCCT/SCMR 2011 Appropriate Use Criteria for Echocardiography. A Report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, American Society of Echocardiography, American Heart Association, American Society of Nuclear Cardiology, Heart Failure Society of America, Heart Rhythm Society, Society for Cardiovascular Angiography and Interventions, Society of Critical Care Medicine, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance American College of Chest Physicians. J Am Soc Echocardiogr 2011;24:229-67.
17. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015;349:255-60.
18. O'Connor AM, Wray J, Tomlinson RS, et al. Impact of surgical complexity on healthrelated quality of life in congenital heart disease surgical survivors. J Am Heart Assoc 2016;5:e001234.
19. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: Systematic review. BMJ 2016;353:i2416.
20. van Loo HM, van den Heuvel ER, Schoevers RA, et al. Sex dependent risk factors for mortality after myocardial infarction: Individual patient data meta-analysis. BMC Med 2014;12:242.
21. Ojeda FM, Müller C, Börnigen D, et al. Comparison of cox model methods in a low-dimensional setting with few events. Genomics Proteomics Bioinformatics 2016;14:235-43.
22. Alonso-Betanzos A, Bolón-Canedo V, Heyndrickx GR, et al. Exploring guidelines for classification of major heart failure subtypes by using machine learning. Clin Med Insights Cardiol 2015;9:57-71.
23. Shameer K, Pugalenthi G, Kandaswamy KK, et al. Insights into protein sequence and structure-derived features mediating 3d domain swapping mechanism using support vector machine based approach. Bioinform Biol Insights 2010;4:BBI.S4464-42.
24. Tylman W, Waszyrowski T, Napieralski A, et al. Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks. Comput Biol Med 2016;69:245-53.
25. Ruiz-Fernández D, Monsalve Torra A, Soriano-Payá A, et al. Aid decision algorithms to estimate the risk in congenital heart surgery. Comput Methods Programs Biomed 2016;126:118-27.
26. Shameer K, Pugalenthi G, Kandaswamy KK, et al. 3dswap-pred: Prediction of 3D domain swapping from protein sequence using Random Forest approach. Protein Pept Lett 2011;18:1010-20.
27. Xiong G, Kola D, Heo R, et al. Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal 2015;24:77-89.
28. Almeida VG, Borba J, Pereira HC, et al. Cardiovascular risk analysis by means of pulse morphology and clustering methodologies. Comput Methods Programs Biomed 2014;117:257-66.
29. Zhang X, Ambale-Venkatesh B, Bluemke DA, et al. Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. J Transl Med 2015;13:343.
30. Lagerholm M, Peterson C, Braccini G, et al. Clustering ECG complexes using hermite functions and self-organizing maps. IEEE Trans Biomed Eng 2000;47:838-48.
31. Narula S, Shameer K, Salem Omar AM, et al. Reply: Deep learning with unsupervised feature in echocardiographic imaging. J Am Coll Cardiol 2017;69:2101-2.
32. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221-48.
33. Gao X, Li W, Loomes M, et al. A fused deep learning architecture for viewpoint classification of echocardiography. Information Fusion 2017;36:103-13.
34. A combined multi-scale deep learning and random forests approach for direct left ventricular volumes estimation in 3D echocardiography: 2016 Computing in Cardiology Conference (CinC); 2016 11-14 Sept, 2016.
35. A left ventricular segmentation method on 3D echocardiography using deep learning and snake: 2016 Computing in Cardiology Conference (CinC); 2016 11-14 Sept, 2016.
36. A reinforcement learning framework for medical image segmentation: The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006.
37. Lee EK, Kurokawa YK, Tu R, et al. Machine learning plus optical flow: A simple and sensitive method to detect cardioactive drugs. Sci Rep 2015;5:11817.
38. Post cardiac surgery recovery process with reinforcement learning: 2015 19th International Conference on System Theory, Control and Computing (ICSTCC); 2015 14-16 Oct, 2015.
39. Zhao Y, Zeng D, Socinski MA, et al. Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer. Biometrics 2011;67:1422-33.
40. Goldstein BA, Navar AM, Pencina MJ, et al. Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review. J Am Med Inform Assoc 2017;24:198-208.
41. Frizzell JD, Liang L, Schulte PJ, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: Comparison of machine learning and other statistical approaches. JAMA Cardiol 2017;2:204-9.
42. Mortazavi BJ, Downing NS, Bucholz EM, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes 2016;9:629-40.
43. Janecek A, Gansterer W, Demel MA, et al. On the relationship between feature selection and classification accuracy. Workshop and Conference Proceedings: Journal of Machine Learning Research, 2008;4:90-105.
44. Holmes MV, Dale CE, Zuccolo L, et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ 2014;349:g4164.
45. Holmes MV, Asselbergs FW, Palmer TM, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J 2015;36:539-50.
46. van der Laan SW, Fall T, Soumaré A, et al. Cystatin C and cardiovascular disease: A mendelian randomization study. J Am Coll Cardiol 2016;68:934-45.
47. Hemani G, Bowden J, Haycock PC, et al. Automating mendelian randomization through machine learning to construct a putative causal map of the human phenome. bioRxiv 2017.
48. Shameer K, Badgeley MA, Miotto R, et al. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief Bioinform 2017;18:105-24.
49. George EPB. Science and statistics. Journal of the American Statistical Association, 1976;71:791-9.