Big Data in Cardiovascular Disease

Cardiovascular diseases exert a wide-reaching epidemiological impact as the number one cause of death worldwide. Emerging technologies such as big data and artificial intelligence (AI) are poised to significantly change the field of cardiology. However, their applications are still emerging. We aimed to define the role of big data and AI in cardiovascular disease with a focus on research. There are zettabyte levels (1021 bytes) of big data in the US that can be directed towards healthcare research. There are applications of big data analytics already being put to use with genomics, heart failure readmissions, echocardiography, and many other areas within cardiology. We profile in this paper an extensive listing of various datasets used throughout the globe to study big data. Within cardiology, there is tremendous potential for the application of big data analytics in personalized patient care; however, they still require validation.

[1]  C. Krittanawong,et al.  Artificial Intelligence in Precision Cardiovascular Medicine. , 2017, Journal of the American College of Cardiology.

[2]  O. Simonetti,et al.  Feasibility, accuracy, and reproducibility of real-time full-volume 3D transthoracic echocardiography to measure LV volumes and systolic function: a fully automated endocardial contouring algorithm in sinus rhythm and atrial fibrillation. , 2012, JACC. Cardiovascular imaging.

[3]  J. Dudley,et al.  Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. , 2016, Circulation. Cardiovascular imaging.

[4]  Nicolas Duchateau,et al.  Characterization of myocardial motion patterns by unsupervised multiple kernel learning , 2017, Medical Image Anal..

[5]  I. Kohane,et al.  Finding the missing link for big biomedical data. , 2014, JAMA.

[6]  G. Moneta,et al.  Rosuvastatin to Prevent Vascular Events in Men and Women with Elevated C-Reactive Protein , 2009 .

[7]  Michael A. Burke,et al.  Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction , 2015, Circulation.

[8]  Spiros C. Denaxas,et al.  Big data from electronic health records for early and late translational cardiovascular research: challenges and potential , 2017, European heart journal.

[9]  S. Negahban,et al.  Analysis of Machine Learning Techniques for Heart Failure Readmissions , 2016, Circulation. Cardiovascular quality and outcomes.

[10]  Mary E. Haas,et al.  Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations , 2018, Nature Genetics.

[11]  Tianxi Cai,et al.  A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program , 2018, Clinical epidemiology.

[12]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[13]  Rachel L. Goldfeder,et al.  Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study , 2017, JAMA cardiology.

[14]  J. Olgin,et al.  Volunteer Participation in the Health eHeart Study: A Comparison with the US Population , 2017, Scientific Reports.

[15]  Kipp W. Johnson,et al.  The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring. , 2018, Human molecular genetics.

[16]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[17]  Francis X. Diebold,et al.  On the Origin(s) and Development of the Term 'Big Data' , 2012 .

[18]  R. Morrison,et al.  Projected Costs of Informal Caregiving for Cardiovascular Disease: 2015 to 2035 A Policy Statement From the American Heart Association , 2018, Circulation.

[19]  Eliot L. Siegel,et al.  Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging , 2018, Current Cardiology Reports.

[20]  Barbara Hammer,et al.  How to Visualize Large Data Sets? , 2012, WSOM.

[21]  Karen Tu,et al.  The Cardiovascular Health in Ambulatory Care Research Team (CANHEART): Using Big Data to Measure and Improve Cardiovascular Health and Healthcare Services , 2015, Circulation. Cardiovascular quality and outcomes.

[22]  R Bellazzi,et al.  Big Data and Biomedical Informatics: A Challenging Opportunity , 2014, Yearbook of Medical Informatics.

[23]  Dipak Kalra,et al.  Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER) , 2012, International journal of epidemiology.

[24]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[25]  A. Su,et al.  Harnessing the heart of big data. , 2015, Circulation research.

[26]  Simon G. Thompson,et al.  UK Biobank comes of age , 2015, The Lancet.

[27]  William S Weintraub,et al.  Translational Medicine in the Era of Big Data and Machine Learning. , 2018, Circulation research.

[28]  Michael J Pencina,et al.  Cardiovascular Disease Risk Assessment: Insights from Framingham. , 2013, Global heart.

[29]  Mark Braverman,et al.  Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study , 2014, PloS one.

[30]  Kipp W. Johnson,et al.  Artificial Intelligence in Cardiology. , 2018, Journal of the American College of Cardiology.

[31]  K. Lunetta,et al.  Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation , 2017, Circulation.

[32]  Cecília M Costa,et al.  The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction. , 2018, Journal of electrocardiology.

[33]  Kipp W. Johnson,et al.  Big data, artificial intelligence, and cardiovascular precision medicine , 2018, Expert Review of Precision Medicine and Drug Development.

[34]  Nicolas Duchateau,et al.  Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy , 2018, European journal of heart failure.

[35]  Mary Brophy,et al.  Million Veteran Program: A mega-biobank to study genetic influences on health and disease. , 2016, Journal of clinical epidemiology.

[36]  H. Krumholz.  Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. , 2014, Health affairs.

[37]  J. Rumsfeld,et al.  Big data analytics to improve cardiovascular care: promise and challenges , 2016, Nature Reviews Cardiology.

[38]  Chi-Ming Chow,et al.  Automation, machine learning, and artificial intelligence in echocardiography: A brave new world , 2018, Echocardiography.

[39]  P. Vardas,et al.  Big Data in Cardiovascular Disease. , 2017, European heart journal.

[40]  Joonseok Kim,et al.  Big Data, Health Informatics, and the Future of Cardiovascular Medicine. , 2017, Journal of the American College of Cardiology.

[41]  Kurt S. Hoffmayer,et al.  Physician Accuracy in Interpreting Potential ST‐Segment Elevation Myocardial Infarction Electrocardiograms , 2013, Journal of the American Heart Association.

[42]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[43]  D. Bates,et al.  Big data in health care: using analytics to identify and manage high-risk and high-cost patients. , 2014, Health affairs.

[44]  Michelle Dunn,et al.  The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data , 2014, J. Am. Medical Informatics Assoc..

[45]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[46]  Kipp W. Johnson,et al.  Machine learning in cardiovascular medicine: are we there yet? , 2018, Heart.