학술논문
Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
Document Type
Article
Author
Montomoli, Jonathan; Romeo, Luca; Moccia, Sara; Bernardini, Michele; Migliorelli, Lucia; Berardini, Daniele; Donati, Abele; Carsetti, Andrea; Bocci, Maria Grazia; Wendel Garcia, Pedro David; Fumeaux, Thierry; Guerci, Philippe; Schüpbach, Reto Andreas; Ince, Can; Frontoni, Emanuele; Hilty, Matthias Peter; Alfaro-Farias, Mario; Vizmanos-Lamotte, Gerardo; Tschoellitsch, Thomas; Meier, Jens; Aguirre-Bermeo, Hernán; Apolo, Janina; Martínez, Alberto; Jurkolow, Geoffrey; Delahaye, Gauthier; Novy, Emmanuel; Losser, Marie-Reine; Wengenmayer, Tobias; Rilinger, Jonathan; Staudacher, Dawid L.; David, Sascha; Welte, Tobias; Stahl, Klaus; Pavlos”, “Agios; Aslanidis, Theodoros; Korsos, Anita; Babik, Barna; Nikandish, Reza; Rezoagli, Emanuele; Giacomini, Matteo; Nova, Alice; Fogagnolo, Alberto; Spadaro, Savino; Ceriani, Roberto; Murrone, Martina; Wu, Maddalena A.; Cogliati, Chiara; Colombo, Riccardo; Catena, Emanuele; Turrini, Fabrizio; Simonini, Maria Sole; Fabbri, Silvia; Potalivo, Antonella; Facondini, Francesca; Gangitano, Gianfilippo; Perin, Tiziana; Grazia Bocci, Maria; Antonelli, Massimo; Gommers, Diederik; Rodríguez-García, Raquel; Gámez-Zapata, Jorge; Taboada-Fraga, Xiana; Castro, Pedro; Tellez, Adrian; Lander-Azcona, Arantxa; Escós-Orta, Jesús; Martín-Delgado, Maria C.; Algaba-Calderon, Angela; Franch-Llasat, Diego; Roche-Campo, Ferran; Lozano-Gómez, Herminia; Zalba-Etayo, Begoña; Michot, Marc P.; Klarer, Alexander; Ensner, Rolf; Schott, Peter; Urech, Severin; Zellweger, Nuria; Merki, Lukas; Lambert, Adriana; Laube, Marcus; Jeitziner, Marie M.; Jenni-Moser, Beatrice; Wiegand, Jan; Yuen, Bernd; Lienhardt-Nobbe, Barbara; Westphalen, Andrea; Salomon, Petra; Drvaric, Iris; Hillgaertner, Frank; Sieber, Marianne; Dullenkopf, Alexander; Petersen, Lina; Chau, Ivan; Ksouri, Hatem; Sridharan, Govind Oliver; Cereghetti, Sara; Boroli, Filippo; Pugin, Jerome; Grazioli, Serge; Rimensberger, Peter C.; Bürkle, Christian; Marrel, Julien; Brenni, Mirko; Fleisch, Isabelle; Lavanchy, Jerome; Perez, Marie-Helene; Ramelet, Anne-Sylvie; Weber, Anja Baltussen; Gerecke, Peter; Christ, Andreas; Ceruti, Samuele; Glotta, Andrea; Marquardt, Katharina; Shaikh, Karim; Hübner, Tobias; Neff, Thomas; Redecker, Hermann; Moret-Bochatay, Mallory; Bentrup, FriederikeMeyer zu; Studhalter, Michael; Stephan, Michael; Brem, Jan; Gehring, Nadine; Selz, Daniela; Naon, Didier; Kleger, Gian-Reto; Pietsch, Urs; Filipovic, Miodrag; Ristic, Anette; Sepulcri, Michael; Heise, Antje; Franchitti Laurent, Marilene; Laurent, Jean-Christophe; Wendel Garcia, Pedro D.; Schuepbach, Reto; Heuberger, Dorothea; Bühler, Philipp; Brugger, Silvio; Fodor, Patricia; Locher, Pascal; Camen, Giovanni; Gaspert, Tomislav; Jovic, Marija; Haberthuer, Christoph; Lussman, Roger F.; Colak, Elif
Source
Journal of Intensive Medicine; October 2021, Vol. 1 Issue: 2 p110-116, 7p
Subject
Language
ISSN
2667100X
Abstract
Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.