Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, et al. 2014 AHA/ACC Guideline for the management of patients with non-ST-elevation acute coronary syndromes: Executive summary. Circulation. Lippincott Williams and Wilkins; 2014. p. 2354–94.
Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent st segment elevation: Task force for the management of acute coronary syndromes in patients presenting without persistent ST segment elevation of. Eur. Heart J. Oxford University Press; 2016. p. 267–315.
Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B, et al. 2015 ACC/AHA/SCAI focused update on primary percutaneous coronary intervention for patients with ST-elevation myocardial infarctionAn update of the 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention and the 2013 ACCF/AHA guideline for the. Circulation. Lippincott Williams and Wilkins. 2016;133:1135–47.
Myerburg RJ, Reddy V, Castellanos A. Indications for Implantable Cardioverter-Defibrillators Based on Evidence and Judgment. J Am Coll Cardiol. 2009;747–63.
Samuel AL. Some Studies in Machine Learning Using the Game of Checkers. IBM J Res Dev. 1959;3:211–29.
Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001;54:979–85.
Singh R, Mukhopadhyay K. Survival analysis in clinical trials: Basics and must know areas. Perspect Clin Res. Medknow. 2011;2:145.
Bewick V, Cheek L, Ball J. Statistics review 14: Logistic Regression. Crit Care. 2005;9:112–8.
Killip T, Kimball JT. Treatment of myocardial infarction in a coronary care unit: A Two year experience with 250 patients. Am J Cardiol. Excerpta Medica. 1967;20:457–64.
D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: The Framingham heart study. Circulation. 2008;117:743–53.
Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van De Werf F, et al. A validated prediction model for all forms of acute coronary syndrome estimating the risk of 6-month postdischarge death in an international registry. J Am Med Assoc. 2004;291:2727–33.
Antman EM, Cohen M, Bernink PJLM, McCabe CH, Horacek T, Papuchis G, et al. The TIMI Risk Score for Unstable Angina/Non–ST Elevation MI. JAMA. American Medical Association. 2000;284:835.
Morrow DA, Antman EM, Parsons L, De Lemos JA, Cannon CP, Giugliano RP, et al. Application of the TIMI risk score for ST-elevation MI in the National Registry of Myocardial Infarction 3. J Am Med Assoc. American Medical Association. 2001;286:1356–9.
Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, et al. Predicting survival in heart failure: A risk score based on 39,372 patients from 30 studies. Eur Heart J. 2013;34:1404–13.
Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation. 2006;113:1424–33.
Mozaffarian D, Anker SD, Anand I, Linker DT, Sullivan MD, Cleland JGF, et al. Prediction of mode of death in heart failure: the Seattle Heart Failure Model. Circulation. 2007;116:392–8.
Sze V, Chen Y-H, Yang T-J, Emer J. Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proc IEEE. 2017;105:2295–329.
Wasserman PD, Schwartz T. Neural networks. II. What are they and why is everybody so interested in them now? IEEE Expert. 1988;3:10–5.
Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern. Springer-Verlag. 1980;36:193–202.
Dechter R. Learning While Searching in Constraint-Satisfaction-Problems. Proc 5th Natl Conf Artif Intell. 1986. p. 178–83.
Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signals Syst. 1989;2:303–14.
Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks. 1991;4:251–7.
•• Myers P., Ng K, Severson K, Kartoun U, Dai W, Huang W, et al. Identifying Unreliable Predictions in Clinical Risk Models. npj Digit Med. 2020;3. This article presents a novel method for evaluating model reliability in its prediction on a a specific patient, a key step towards bringing deep models into clinical practice.
Lipton ZC. The Mythos of Model Interpretability. Commun ACM. 2018;61:35–43.
Kalisch M, Bühlmann P. Causal structure learning and inference: A selective review. Qual Technol Quant Manag. Chung Hua University. 2014;11:3–21.
Shapely LS. A value for n-person games. Contrib Theory Games. 1953;2:307–17.
Lundberg SM, Allen PG, Lee S-I. A Unified Approach to Interpreting Model Predictions. Adv Neural Inf Process Syst 30. 2017.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proc IEEE Int Conf Comput Vis. Institute of Electrical and Electronics Engineers Inc.; 2017. p. 618–26.
Simonyan K, Vedaldi A, Zisserman A. Deep Inside Convolutional Networks: Visualizing Image Classification Models and Saliency Maps. Int Conf Learn Represent. 2013.
• Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. Springer Science and Business Media LLC; 2019;1:206–15. In this perspective, Cynthia Rudin make a compelling argument regarding common approaches to making deep models, or “black boxes,” explainable. She suggests that thos working in the field should focus on developing tools which are inherently interpretable, contrary to dominant trends in machine learning research.
Wang F, Kaushal R, Khullar D. Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine? Ann Intern Med. 2020;172:59.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis. Springer New York LLC. 2015;115:211–52.
Cruz-Roa AA, Arevalo Ovalle JE, Madabhushi A, González Osorio FA. A deep learning architeczture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2013. p. 403–10.
Sirinukunwattana K, Raza SEA, Tsang YW, Snead DRJ, Cree IA, Rajpoot NM. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans Med Imaging. Institute of Electrical and Electronics Engineers Inc. 2016;35:1196–206.
Albarqouni S, Zurich E, Achilles F, Belagiannis V, Demirci S, Baur C, et al. AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images. IEEE Trans Med Imaging. 2016;35:1321.
Sun W, Zheng B, Qian W. Computer aided lung cancer diagnosis with deep learning algorithms. In: Tourassi GD, Armato SG, editors. Proc SPIE. 2016. p. 97850Z.
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. 2017;
Jo T, Nho K, Saykin AJ. Deep Learning in Alzheimer’s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. Front Aging Neurosci. Frontiers Media S.A.; 2019;11.
Hubel DH, Wiesel T. Receptive fields of single neurones in the cat’s straite cortex. J Physiol. 1959;148:574–91.
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. MIT Press - Journals. 1989;1:541–51.
Tsay D, Patterson C. From Machine Learning to Artificial Intelligence Applications in Cardiac Care. Circulation. 2018;138:2569–75.
Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med. Springer Nature. 2018;1.
Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy. Circulation. Lippincott Williams and Wilkins. 2018;138:1623–35.
Dormer JD, Fei B, Halicek M, Ma L, Reilly CM, Schreibmann E. Heart chamber segmentation from CT using convolutional neural networks. SPIE-Intl Soc Optical Eng. 2018;100.
Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. Elsevier B.V. 2016;30:108–19.
Oksuz I, Ruijsink B, Puyol-Antón E, Clough JR, Cruz G, Bustin A, et al. Automatic CNN-based detection of cardiac MR motion artifacts using k-space data augmentation and curriculum learning. Med Image Anal. Elsevier B.V. 2019;55:136–47.
•• Poplin R, Varadarajan A V., Blumer K, Liu Y, McConnell M V., Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. Nature Publishing Group; 2018;2:158–64. An interesting study that uses deep neural network models to predict cardiovascular outcomes from retinal images. Unlike many approaches, the authors also use methods to help understand why the model arrives at a given result.
Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J. 2003;24:987–1003.
Mayampurath A, Sanchez-Pinto LN, Carey KA, Venable L-R, Churpek M. Combining patient visual timelines with deep learning to predict mortality. Raza M, editor. PLoS One. 2019;14:e0220640.
Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: A review. J Neural Eng. Institute of Physics Publishing; 2019.
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat. Med. Nature Publishing Group; 2019. p. 70–4.
Echouffo-Tcheugui JB, Erqou S, Butler J, Yancy CW, Fonarow GC. Assessing the Risk of Progression From Asymptomatic Left Ventricular Dysfunction to Overt Heart Failure: A Systematic Overview and Meta-Analysis. JACC Hear Fail. Elsevier Inc. 2016;4:237–48.
Myers PD, Scirica BM, Stultz CM. Machine Learning Improves Risk Stratification After Acute Coronary Syndrome. Sci Rep. Nature Publishing Group. 2017;7:12692.
Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. Nature Publishing Groups; 2016. p. 3.