Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis
暂无分享,去创建一个
M. Motwani | D. Dey | D. Berman | G. Germano | S. Achenbach | M. Al-mallah | D. Andreini | M. Budoff | Filippo Cademartiri | T. Callister | Hyuk-Jae Chang | K. Chinnaiyan | B. Chow | R. Cury | A. Delago | M. Gomez | H. Gransar | M. Hadamitzky | J. Hausleiter | Niree Hindoyan | G. Feuchtner | P. Kaufmann | Yong-Jin Kim | J. Leipsic | F. Lin | E. Maffei | H. Marques | G. Pontone | G. Raff | R. Rubinshtein | L. Shaw | J. Stehli | T. Villines | A. Dunning | J. Min | P. Slomka | Millie J. Gomez | M. Al-Mallah | Yong‐Jin Kim | H. Chang | H. Chang | J. Min | M. Budoff | Niree A. Hindoyan
[1] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .
[2] E. DeLong,et al. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.
[3] M Greiner,et al. A modified ROC analysis for the selection of cut-off values and the definition of intermediate results of serodiagnostic tests. , 1995, Journal of immunological methods.
[4] D. Levy,et al. Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.
[5] D. Berman,et al. Incremental prognostic value of myocardial perfusion single photon emission computed tomography in patients with diabetes mellitus. , 1999, American heart journal.
[6] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[7] Thomas G. Dietterich. Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.
[8] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[9] Ian Witten,et al. Data Mining , 2000 .
[10] E Mjolsness,et al. Machine learning for science: state of the art and future prospects. , 2001, Science.
[11] A. Dyer,et al. Major Risk Factors as Antecedents of Fatal and Nonfatal Coronary Heart Disease Events , 2003 .
[12] A. Dyer,et al. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. , 2003, JAMA.
[13] Geoff Holmes,et al. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..
[14] Annette M. Molinaro,et al. Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..
[15] Takafumi Kanamori,et al. Robust Loss Functions for Boosting , 2007, Neural Computation.
[16] Daniel S Berman,et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. , 2007, Journal of the American College of Cardiology.
[17] Patrick W Serruys,et al. Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. , 2008, Journal of the American College of Cardiology.
[18] M. Budoff,et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Indi , 2008, Journal of the American College of Cardiology.
[19] Jörg Hausleiter,et al. Prognostic value of coronary computed tomographic angiography for prediction of cardiac events in patients with suspected coronary artery disease. , 2009, JACC. Cardiovascular imaging.
[20] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[21] Harald Binder,et al. Boosting for high-dimensional time-to-event data with competing risks , 2009, Bioinform..
[22] Sanjay Kaul,et al. Low diagnostic yield of elective coronary angiography. , 2010, The New England journal of medicine.
[23] Akbar K Waljee,et al. Machine Learning in Medicine: A Primer for Physicians , 2010, The American Journal of Gastroenterology.
[24] S. Abbara. SCCT guidelines for performance of coronary computed tomographic angiography: A report of the Society of Cardiovascular Computed Tomography Guidelines Committee , 2010 .
[25] Ian H. Witten,et al. Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .
[26] Yeung Yam,et al. Incremental Prognostic Value of Cardiac Computed Tomography in Coronary Artery Disease Using CONFIRM: COroNary Computed Tomography Angiography Evaluation for Clinical Outcomes: An InteRnational Multicenter Registry , 2011, Circulation. Cardiovascular imaging.
[27] M. Pencina,et al. Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry. , 2011, Journal of cardiovascular computed tomography.
[28] Ian H. Witten,et al. Chapter 1 – What's It All About? , 2011 .
[29] Usama Bilal,et al. Challenges and opportunities for cardiovascular disease prevention. , 2011, The American journal of medicine.
[30] D. Berman,et al. Optimized prognostic score for coronary computed tomographic angiography: results from the CONFIRM registry (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter Registry). , 2013, Journal of the American College of Cardiology.
[31] Daniel S. Berman,et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population , 2015, Journal of Nuclear Cardiology.
[32] Michiel L Bots,et al. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. , 2014, International journal of cardiology.
[33] V. Fuster,et al. The myth of the "vulnerable plaque": transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. , 2015, Journal of the American College of Cardiology.
[34] K. Borgwardt,et al. Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.