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• W4224979105 abstract "HomeCirculation: Cardiovascular Quality and OutcomesVol. 15, No. 6Extracting More From Less: A New Frontier for High-Throughput Clinical Phenotyping No AccessEditorialRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessEditorialRequest AccessFull TextExtracting More From Less: A New Frontier for High-Throughput Clinical Phenotyping David Ouyang and Susan Cheng David OuyangDavid Ouyang Correspondence to: David Ouyang, MD, Department of Cardiology, Smidt Heart Institute, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90048. Email E-mail Address: [email protected] https://orcid.org/0000-0002-3813-7518 Department of Cardiology, Smidt Heart Institute (D.O., S.C.), Cedars-Sinai Medical Center, Los Angeles, CA. Division of Artificial Intelligence in Medicine (D.O.), Cedars-Sinai Medical Center, Los Angeles, CA. Search for more papers by this author and Susan ChengSusan Cheng https://orcid.org/0000-0002-4977-036X Department of Cardiology, Smidt Heart Institute (D.O., S.C.), Cedars-Sinai Medical Center, Los Angeles, CA. Search for more papers by this author Originally published28 Apr 2022https://doi.org/10.1161/CIRCOUTCOMES.122.009055Circulation: Cardiovascular Quality and Outcomes. 2022;15This article is a commentary on the followingCardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic PredictorsExtracting More From Less: A New Frontier for High-Throughput Clinical Phenotyping. Circulation: Cardiovascular Quality and Outcomes, 15(6), pp. e009055FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.For Disclosures, see page 392.Correspondence to: David Ouyang, MD, Department of Cardiology, Smidt Heart Institute, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, Los Angeles, CA 90048. Email david.[email protected]orgReferences1. Chen JH, Asch SM. Machine eearning and prediction in medicine - beyond the peak of inflated expectations.N Engl J Med. 2017; 376:2507–2509. doi: 10.1056/NEJMp1702071CrossrefMedlineGoogle Scholar2. Ouyang D, Zou J. Deep learning models to detect hidden clinical correlates.Lancet Digit Health. 2020; 2:e334–e335. doi: 10.1016/S2589-7500(20)30138-2CrossrefMedlineGoogle Scholar3. Goto S, Homilius M, John JE, Truslow JG, Werdich AA, Blood AJ, Park BH, MacRae CA, Deo RC. Artificial intelligence-enabled event adjudication: estimating delayed cardiovascular effects of respiratory viruses [Internet].bioRxiv. 2020; Available from: http://medrxiv.org/lookup/doi/10.1101/2020.11.12.20230706Google Scholar4. Cheng S, Fernandes VR, Bluemke DA, McClelland RL, Kronmal RA, Lima JA. Age-related left ventricular remodeling and associated risk for cardiovascular outcomes: the Multi-Ethnic Study of Atherosclerosis.Circ Cardiovasc Imaging. 2009; 2:191–198. doi: 10.1161/CIRCIMAGING.108.819938LinkGoogle Scholar5. Truslow JG, Goto S, Homilius M, Mow C, Higgins JM, MacRae CA, Deo RC. Cardiovascular risk assessment using artificial intelligence-enabled event adjudication and hematologic predictors.Circ Cardiovasc Qual Outcomes. 2022; 15:377–390. doi: 10.1161/CIRCOUTCOMES.121.008007.LinkGoogle Scholar6. Kwon JM, Cho Y, Jeon KH, Cho S, Kim KH, Baek SD, Jeung S, Park J, Oh BH. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study.Lancet Digit Health. 2020; 2:e358–e367. doi: 10.1016/S2589-7500(20)30108-4CrossrefMedlineGoogle Scholar7. Hughes JW, Yuan N, He B, Ouyang J, Ebinger J, Botting P, Lee J, Theurer J, Tooley JE, Nieman K, et al. Deep learning evaluation of biomarkers from echocardiogram videos.EBioMed. 2021; 73:103613. doi: 10.1016/j.ebiom.2021.103613CrossrefMedlineGoogle Scholar8. Ponikowski P, Kirwan BA, Anker SD, McDonagh T, Dorobantu M, Drozdz J, Fabien V, Filippatos G, Göhring UM, Keren A, et al; AFFIRM-AHF investigators. Ferric carboxymaltose for iron deficiency at discharge after acute heart failure: a multicentre, double-blind, randomised, controlled trial.Lancet. 2020; 396:1895–1904. doi: 10.1016/S0140-6736(20)32339-4CrossrefMedlineGoogle Scholar9. Mazer CD, Whitlock RP, Fergusson DA, Hall J, Belley-Cote E, Connolly K, Khanykin B, Gregory AJ, de Médicis É, McGuinness S, et al; TRICS Investigators and Perioperative Anesthesia Clinical Trials Group. Restrictive or liberal red-cell transfusion for cardiac surgery.N Engl J Med. 2017; 377:2133–2144. doi: 10.1056/NEJMoa1711818CrossrefMedlineGoogle Scholar10. Holst LB, Haase N, Wetterslev J, Wernerman J, Guttormsen AB, Karlsson S, Johansson PI, Aneman A, Vang ML, Winding R, et al; TRISS Trial Group; Scandinavian Critical Care Trials Group. Lower versus higher hemoglobin threshold for transfusion in septic shock.N Engl J Med. 2014; 371:1381–1391. doi: 10.1056/NEJMoa1406617CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesCardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic PredictorsJames G. Truslow, et al. Circulation: Cardiovascular Quality and Outcomes. 2022;15 June 2022Vol 15, Issue 6 Advertisement Article InformationMetrics © 2022 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.122.009055PMID: 35477258 Originally publishedApril 28, 2022 Keywordselectronic health recordsalgorithmsEditorialssciencemedicineartificial intelligencePDF download Advertisement SubjectsDiagnostic Testing" @default.
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