A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records
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Huilong Duan | Wei Dong | Jiquan Liu | Zhengxing Huang | H. Duan | Zhengxing Huang | Jiquan Liu | W. Dong
[1] Chueh-Loo Poh,et al. A novel neural-inspired learning algorithm with application to clinical risk prediction , 2015, J. Biomed. Informatics.
[2] Wei Huang,et al. The expanded Global Registry of Acute Coronary Events: baseline characteristics, management practices, and hospital outcomes of patients with acute coronary syndromes. , 2009, American heart journal.
[3] Svetha Venkatesh,et al. Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM) , 2015, J. Biomed. Informatics.
[4] I. Graham,et al. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. , 2009, Journal of the American College of Cardiology.
[5] Constantinos S. Pattichis,et al. Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees , 2010, IEEE Transactions on Information Technology in Biomedicine.
[6] Chunhua Weng,et al. Facilitating biomedical researchers' interrogation of electronic health record data: Ideas from outside of biomedical informatics , 2016, J. Biomed. Informatics.
[7] Paulo Carvalho,et al. The CardioRisk project: Improvement of cardiovascular risk assessment , 2015, J. Comput. Sci..
[8] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[9] E W Steyerberg,et al. Predictors of outcome in patients with acute coronary syndromes without persistent ST-segment elevation. Results from an international trial of 9461 patients. The PURSUIT Investigators. , 2000, Circulation.
[10] T. Thom,et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee : Heart disease and stroke statistical-2006 update : A report from the American Heart Association Statistics Committee and Stroke statistics subcommittee , 2006 .
[11] Cynthia Brandt,et al. Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management , 2013, J. Biomed. Informatics.
[12] Naif Alajlan,et al. Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..
[13] Huilong Duan,et al. Predictive monitoring of clinical pathways , 2016, Expert Syst. Appl..
[14] Paul Burton,et al. Rivaroxaban in patients with a recent acute coronary syndrome. , 2012, The New England journal of medicine.
[15] S. Holm. A Simple Sequentially Rejective Multiple Test Procedure , 1979 .
[16] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Huilong Duan,et al. On mining latent treatment patterns from electronic medical records , 2015, Data Mining and Knowledge Discovery.
[18] J Nikki McKoy,et al. Systematic Review of Cardiovascular Disease Risk Assessment Tools , 2011 .
[19] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[20] Ting Chen,et al. Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[21] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[22] E. Antman,et al. The TIMI risk score for unstable angina/non-ST elevation MI: A method for prognostication and therapeutic decision making. , 2000, JAMA.
[23] Gabriele Eisenhauer. Risk Stratification A Practical Guide For Clinicians , 2016 .
[24] Jennifer G. Robinson,et al. 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 , 2014, Circulation.
[25] Huilong Duan,et al. Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome , 2016, International journal of environmental research and public health.
[26] Gediminas Adomavicius,et al. Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data , 2014, Data Mining and Knowledge Discovery.
[27] T. Ivanova,et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice , 2016 .
[28] Jonathan M. Garibaldi,et al. A hybrid model for automatic identification of risk factors for heart disease , 2015, J. Biomed. Informatics.
[29] Yoshua Bengio,et al. Inference for the Generalization Error , 1999, Machine Learning.
[30] D. Levy,et al. Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.
[31] Giulio Guagliumi,et al. Comparative early and late outcomes after primary percutaneous coronary intervention in ST-segment elevation and non-ST-segment elevation acute myocardial infarction (from the CADILLAC trial). , 2006, The American journal of cardiology.
[32] Manabu Torii,et al. Risk factor detection for heart disease by applying text analytics in electronic medical records , 2015, J. Biomed. Informatics.
[33] May D. Wang,et al. –Omic and Electronic Health Record Big Data Analytics for Precision Medicine , 2017, IEEE Transactions on Biomedical Engineering.
[34] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[35] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[36] N. Graham,et al. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .
[37] Li Zhang,et al. SD-MSAEs: Promoter recognition in human genome based on deep feature extraction , 2016, J. Biomed. Informatics.
[38] Huilong Duan,et al. A probabilistic topic model for clinical risk stratification from electronic health records , 2015, J. Biomed. Informatics.
[39] Zhengxing Huang,et al. MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records , 2017, J. Biomed. Informatics.
[40] Alan D. Lopez,et al. Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study , 1997, The Lancet.
[41] T Fahey,et al. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review , 2006, Heart.
[42] Aidong Zhang,et al. Identifying informative risk factors and predicting bone disease progression via deep belief networks. , 2014, Methods.
[43] Björn W. Schuller,et al. Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition , 2014, IEEE Signal Processing Letters.
[44] Matthew Clark,et al. Prediction of clinical risks by analysis of preclinical and clinical adverse events , 2015, J. Biomed. Informatics.
[45] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[46] Shah Ebrahim,et al. European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force of European and Other Societies on Cardiovascular Disease Prevention in Clinical Practice. , 2003 .
[47] Ping Zhang,et al. Risk Prediction with Electronic Health Records: A Deep Learning Approach , 2016, SDM.
[48] M. Drazner,et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. , 2013, Journal of the American College of Cardiology.