{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:34:38Z","timestamp":1784957678325,"version":"3.55.0"},"reference-count":52,"publisher":"Elsevier BV","issue":"23","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T00:00:00Z","timestamp":1523577600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacc.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American College of Cardiology"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1016\/j.jacc.2018.03.521","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T18:32:36Z","timestamp":1528223556000},"page":"2668-2679","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1068,"title":["Artificial Intelligence in Cardiology"],"prefix":"10.1016","volume":"71","author":[{"given":"Kipp W.","family":"Johnson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Torres Soto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin S.","family":"Glicksberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khader","family":"Shameer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Miotto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsin","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euan","family":"Ashley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel T.","family":"Dudley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jacc.2018.03.521_bib1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1182\/blood-2017-03-734533","article-title":"The relative utilities of genome-wide, gene panel, and individual gene sequencing in clinical practice","volume":"130","author":"Kuo","year":"2017","journal-title":"Blood"},{"key":"10.1016\/j.jacc.2018.03.521_bib2","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/S0140-6736(17)30154-X","article-title":"Towards a smart medical home","volume":"389","author":"Muse","year":"2017","journal-title":"Lancet"},{"key":"10.1016\/j.jacc.2018.03.521_bib3","doi-asserted-by":"crossref","first-page":"283rv3","DOI":"10.1126\/scitranslmed.aaa3487","article-title":"The emerging field of mobile health","volume":"7","author":"Steinhubl","year":"2015","journal-title":"Sci Transl Med"},{"key":"10.1016\/j.jacc.2018.03.521_bib4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1093\/bib\/bbv118","article-title":"Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams","volume":"18","author":"Shameer","year":"2017","journal-title":"Briefings in Bioinformatics"},{"key":"10.1016\/j.jacc.2018.03.521_bib5","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.jacc.2016.12.024","article-title":"The academic medical system: reinvention to survive the revolution in health care","volume":"69","author":"Konstam","year":"2017","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib6","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.jacc.2015.08.006","article-title":"Moving from digitalization to digitization in cardiovascular care: why is it important, and what could it mean for patients and providers?","volume":"66","author":"Steinhubl","year":"2015","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib7","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.2196\/jmir.4456","article-title":"How\u00a0consumers and physicians view new medical technology: comparative survey","volume":"17","author":"Boeldt","year":"2015","journal-title":"J\u00a0Med Internet Res"},{"key":"10.1016\/j.jacc.2018.03.521_bib8","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1161\/CIRCOUTCOMES.116.003039","article-title":"Analysis of machine learning techniques for heart failure readmissions","volume":"9","author":"Mortazavi","year":"2016","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"10.1016\/j.jacc.2018.03.521_bib9","first-page":"276","article-title":"Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort","volume":"22","author":"Shameer","year":"2016","journal-title":"Pac Symp Biocomput"},{"key":"10.1016\/j.jacc.2018.03.521_bib10","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1002\/(SICI)1097-0258(20000430)19:8<1059::AID-SIM412>3.0.CO;2-0","article-title":"Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets","volume":"19","author":"Steyerberg","year":"2000","journal-title":"Stat Med"},{"key":"10.1016\/j.jacc.2018.03.521_bib11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.7326\/0003-4819-152-4-201002160-00014","article-title":"Prediction rules must be developed according to methodological guidelines","volume":"152","author":"Janssen","year":"2010","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.jacc.2018.03.521_bib12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1097\/CCM.0000000000001571","article-title":"Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards","volume":"44","author":"Churpek","year":"2016","journal-title":"Crit Care Med"},{"key":"10.1016\/j.jacc.2018.03.521_bib13","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1001\/jamacardio.2016.3956","article-title":"Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches","volume":"2","author":"Frizzell","year":"2017","journal-title":"JAMA Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib14","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1503\/cmaj.150430","article-title":"Do clinicians understand the size of treatment effects? A randomized survey across 8 countries","volume":"188","author":"Johnston","year":"2016","journal-title":"CMAJ"},{"key":"10.1016\/j.jacc.2018.03.521_bib15","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1681\/ASN.2015070832","article-title":"Bridging translation by improving preclinical study design in AKI","volume":"26","author":"de Caestecker","year":"2015","journal-title":"J\u00a0Am Soc Nephrol"},{"key":"10.1016\/j.jacc.2018.03.521_bib16","unstructured":"Harrell FE. How To Do Bad Biomarker Research. Vanderbilt Center For Quantitative Sciences Workshop, Nashville, Tennessee, 2015."},{"key":"10.1016\/j.jacc.2018.03.521_bib17","unstructured":"Senn S. Dichotomania: an obsessive compulsive disorder that is badly affecting the quality of analysis of pharmaceutical trials. Presented at: 55th Session of the International Statistical Institute; 2005, Sydney, Australia."},{"key":"10.1016\/j.jacc.2018.03.521_bib18","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1001\/jamacardio.2016.3366","article-title":"Evaluation of a prediction model for the development of atrial fibrillation in a repository of electronic medical records","volume":"1","author":"Kolek","year":"2016","journal-title":"JAMA Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib19","doi-asserted-by":"crossref","first-page":"h3868","DOI":"10.1136\/bmj.h3868","article-title":"How to develop a more accurate risk prediction model when there are few events","volume":"351","author":"Pavlou","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.jacc.2018.03.521_bib20","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1161\/CIRCGENETICS.113.000490","article-title":"Simultaneous consideration of multiple candidate protein biomarkers for long-term risk for cardiovascular events","volume":"8","author":"Halim","year":"2015","journal-title":"Circ Cardiovasc Genet"},{"key":"10.1016\/j.jacc.2018.03.521_bib21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0174944","article-title":"Can machine-learning improve cardiovascular risk prediction using routine clinical data?","volume":"12","author":"Weng","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.jacc.2018.03.521_bib22","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib23","article-title":"Artificial intelligence-based assessment of left ventricular filling pressures from 2-dimensional cardiac ultrasound images","author":"Salem Omar","year":"2017","journal-title":"J\u00a0Am Coll Cardiol Img"},{"key":"10.1016\/j.jacc.2018.03.521_bib24","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.jcin.2017.04.007","article-title":"Plasma phospholipids and sphingolipids identify stent restenosis after percutaneous coronary intervention","volume":"10","author":"Cui","year":"2017","journal-title":"J\u00a0Am Coll Cardiol Intv"},{"key":"10.1016\/j.jacc.2018.03.521_bib25","doi-asserted-by":"crossref","unstructured":"Alexandru Niculescu-Mizil RC. Predicting good probabilities with supervised learning. In: Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005:625\u201332.","DOI":"10.1145\/1102351.1102430"},{"key":"10.1016\/j.jacc.2018.03.521_bib26","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1016\/0735-1097(95)00385-1","article-title":"One-year mortality prognosis in heart failure: a neural network approach based on echocardiographic data","volume":"26","author":"Ortiz","year":"1995","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib27","first-page":"32","article-title":"Risk stratification in heart failure using artificial neural networks","author":"Atienza","year":"2000","journal-title":"Proc AMIA Symp"},{"key":"10.1016\/j.jacc.2018.03.521_bib28","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol Rev"},{"key":"10.1016\/j.jacc.2018.03.521_bib29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.jacc.2018.03.521_bib30","article-title":"Deep learning for healthcare: review, opportunities and challenges","author":"Miotto","year":"2017","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.jacc.2018.03.521_bib31","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1016\/j.jacc.2017.01.062","article-title":"Reply: Deep learning with unsupervised feature in echocardiographic imaging","volume":"69","author":"Narula","year":"2017","journal-title":"J Am Coll Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib32","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.jacc.2018.03.521_bib33","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.jacc.2018.03.521_bib34","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-Time Patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2016","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.jacc.2018.03.521_bib35","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1364\/BOE.8.001203","article-title":"Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography","volume":"8","author":"Abdolmanafi","year":"2017","journal-title":"Biomed Opt Express"},{"key":"10.1016\/j.jacc.2018.03.521_bib36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/jamia\/ocw112","article-title":"Using recurrent neural network models for early detection of heart failure onset","volume":"24","author":"Choi","year":"2017","journal-title":"J\u00a0Am Med Inform Assoc"},{"key":"10.1016\/j.jacc.2018.03.521_bib37","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s12265-016-9727-8","article-title":"Tensor factorization for precision medicine in heart failure with preserved ejection fraction","volume":"10","author":"Luo","year":"2017","journal-title":"J\u00a0Cardiovasc Transl Res"},{"key":"10.1016\/j.jacc.2018.03.521_bib38","doi-asserted-by":"crossref","first-page":"311ra174","DOI":"10.1126\/scitranslmed.aaa9364","article-title":"Identification of type 2 diabetes subgroups through topological analysis of patient similarity","volume":"7","author":"Li","year":"2015","journal-title":"Sci Transl Med"},{"key":"10.1016\/j.jacc.2018.03.521_bib39","doi-asserted-by":"crossref","first-page":"26094","DOI":"10.1038\/srep26094","article-title":"Deep patient: an unsupervised representation to predict the future of patients from the electronic health records","volume":"6","author":"Miotto","year":"2016","journal-title":"Sci Rep"},{"key":"10.1016\/j.jacc.2018.03.521_bib40","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1038\/nrcardio.2016.101","article-title":"Precision medicine in cardiology","volume":"13","author":"Antman","year":"2016","journal-title":"Nat Rev Cardiol"},{"key":"10.1016\/j.jacc.2018.03.521_bib41","first-page":"311","article-title":"Enabling precision cardiology through multiscale biology and systems medicine","volume":"2","author":"Johnson","year":"2017","journal-title":"J\u00a0Am Coll Cardiol Basic Trans Science"},{"key":"10.1016\/j.jacc.2018.03.521_bib42","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s12265-017-9739-z","article-title":"Phenomapping for the identification of hypertensive patients with the myocardial substrate for heart failure with preserved ejection fraction","volume":"10","author":"Katz","year":"2017","journal-title":"J\u00a0Cardiovasc Transl Res"},{"key":"10.1016\/j.jacc.2018.03.521_bib43","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1161\/CIRCULATIONAHA.116.021884","article-title":"Phenotype-specific treatment of heart failure with preserved ejection fraction: a multiorgan roadmap","volume":"134","author":"Shah","year":"2016","journal-title":"Circulation"},{"key":"10.1016\/j.jacc.2018.03.521_bib44","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.hfc.2014.04.008","article-title":"Phenotypic spectrum of heart failure with preserved ejection fraction","volume":"10","author":"Shah","year":"2014","journal-title":"Heart Fail Clin"},{"key":"10.1016\/j.jacc.2018.03.521_bib45","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCHEARTFAILURE.115.003116","article-title":"Predicting heart failure with preserved and reduced ejection fraction: the International Collaboration on Heart Failure Subtypes","volume":"9","author":"Ho","year":"2016","journal-title":"Circ Heart Fail"},{"key":"10.1016\/j.jacc.2018.03.521_bib46","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1161\/CIRCULATIONAHA.114.010637","article-title":"Phenomapping for novel classification of heart failure with preserved ejection fraction","volume":"131","author":"Shah","year":"2015","journal-title":"Circulation"},{"key":"10.1016\/j.jacc.2018.03.521_bib47","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.jacc.2018.03.521_bib48","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of Go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.jacc.2018.03.521_bib49","first-page":"180","article-title":"Causal inference on electronic health records to assess blood pressure treatment targets: an application of the parametric g formula","volume":"23","author":"Johnson","year":"2018","journal-title":"Pac Symp Biocomput"},{"key":"10.1016\/j.jacc.2018.03.521_bib50","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10994-010-5229-0","article-title":"Informing sequential clinical decision-making through reinforcement learning: an empirical study","volume":"84","author":"Shortreed","year":"2011","journal-title":"Mach Learn"},{"key":"10.1016\/j.jacc.2018.03.521_bib51","article-title":"A\u00a0reinforcement learning approach to weaning of mechanical ventilation in intensive care units","author":"Prasad","year":"2017","journal-title":"ArXiv e-prints"},{"key":"10.1016\/j.jacc.2018.03.521_bib52","doi-asserted-by":"crossref","DOI":"10.1136\/heartjnl-2017-311198","article-title":"Machine learning in cardiovascular medicine: are we there yet?","author":"Shameer","year":"2018","journal-title":"Heart"}],"container-title":["Journal of the American College of Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0735109718344085?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0735109718344085?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:15:42Z","timestamp":1760015742000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0735109718344085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6]]},"references-count":52,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["S0735109718344085"],"URL":"https:\/\/doi.org\/10.1016\/j.jacc.2018.03.521","relation":{"has-review":[{"id-type":"doi","id":"10.3410\/f.733421285.793565018","asserted-by":"object"}]},"ISSN":["0735-1097"],"issn-type":[{"value":"0735-1097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in Cardiology","name":"articletitle","label":"Article Title"},{"value":"Journal of the American College of Cardiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacc.2018.03.521","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.","name":"copyright","label":"Copyright"}]}}