Reply: Leveraging Machine Learning to Generate Prediction Models for Structural Valve Interventions
- PMID: 31648770
- PMCID: PMC7011192
- DOI: 10.1016/j.jcin.2019.09.001
Reply: Leveraging Machine Learning to Generate Prediction Models for Structural Valve Interventions
Comment on
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Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.JACC Cardiovasc Interv. 2019 Jul 22;12(14):1328-1338. doi: 10.1016/j.jcin.2019.06.013. JACC Cardiovasc Interv. 2019. PMID: 31320027 Free PMC article.
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Machine Learning for Making Aortic Valve Interventions Complementary and Not Competitive.JACC Cardiovasc Interv. 2019 Oct 28;12(20):2112. doi: 10.1016/j.jcin.2019.08.016. JACC Cardiovasc Interv. 2019. PMID: 31648768 No abstract available.
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Machine Learning Is No Magic: Put a Rabbit Into the Hat Before Pulling it Out.JACC Cardiovasc Interv. 2019 Oct 28;12(20):2112-2113. doi: 10.1016/j.jcin.2019.08.018. JACC Cardiovasc Interv. 2019. PMID: 31648769 No abstract available.
References
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- Mack MJ, Leon MB, Thourani VH, et al. Transcatheter aortic-valve replacement with a balloon-expandable valve in low-risk patients. N Engl J Med 2019;380:1695–705. - PubMed
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- Popma JJ, Deeb GM, Yakubov SJ, et al. Transcatheter aortic-valve replacement with a self-expanding valve in low-risk patients. N Engl J Med 2019;380:1706–15. - PubMed
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- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonpara-metric approach. Biometrics 1988;44:837–45. - PubMed
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