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Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making

Nicolas Allou, Jérôme Allyn, Sophie Provenchère, Benjamin Delmas, Éric Braunberger, Matthieu Oliver, Jean Louis De Brux, Cyril Ferdynus, Paul Achouh, Nicolas Allou, Jérôme Allyn, Stéphane Aubert, Christophe Baufreton, Eric Bezon, Nicolas Bonnet, Olivier Bouchot, Éric Braunberger, Lionel Camilleri, Thierry Caus, Didier Chatel, Nicolas Chavanis, Sidney Chocron, Pierre Corbi, Alain Curtil, Jean Louis De Brux, Philippe Delentdecker, Philippe Deleuze, Benjamin Delmas, Roland Demaria, Patrice Dervanian, Fabien Doguet, Olivier Fabre, Thierry Folliguet, Jean-Marc Frapier, Jean-Philippe Frieh, Jérôme Jouan, Joël Lapeze, Pascal Leprince, Bertrand Marcheix, Juan Pablo Maureira, Jean‐Philippe Mazzucotelli, Patrick Nataf, Jean‐François Obadia, Sophie Provenchère, Jean‐Christian Roussel, Vito Giovanni Ruggieri, Jean‐Philippe Verhoye, André Vincentelli

2023Journal of Thoracic and Cardiovascular Surgery13 citationsDOIOpen Access PDF

Topics & Concepts

MedicineEuroSCOREReceiver operating characteristicCardiac surgeryDeep learningMachine learningArtificial intelligenceGradient boostingRandom forestInternal medicineSurgeryComputer scienceSepsis Diagnosis and TreatmentArtificial Intelligence in Healthcare and EducationCardiac Valve Diseases and Treatments
Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making | Litcius