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Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning

Soufiane Ajana, Audrey Cougnard‐Grégoire, Johanna M. Colijn, B. Merle, Timo Verzijden, Paulus T.V.M. de Jong, Albert Hofman, Johannes Vingerling, Boris P. Hejblum, Jean‐François Korobelnik, Magda A. Meester‐Smoor, Marius Ueffing, Hélène Jacqmin‐Gadda, Caroline C. W. Klaver, Cécile Delcourt, Erkin I. Acar, Blanca Arango‐González, Angela Armento, Franz Badura, Vaibhav Bhatia, Shomi S. Bhattacharya, Marc Biarnés, Anna Borrell, Sofia M. Calado, Sascha Dammeier, Anita de Breuk, Berta de la Cerda, Anneke I. den Hollander, Francisco J. Diaz‐Corrales, Sigrid Diether, Eszter Emri, Tanja Endermann, Lucia L. Ferraro, Míriam Garcia, Thomas J. Heesterbeek, Sabina Honisch, Carel B. Hoyng, Ellen Kilger, Elöd Körtvely, Claire Lastrucci, Hanno Langen, Imre Lengyel, Philip J. Luthert, Jordi Monés, Everson Nogoceke, Tünde Pető, Frances M. Pool, Eduardo Rodríguez‐Bocanegra, Luís Serrano, José Sousa, Eric F. Thee, Marius Ueffing, Karl Ulrich Bartz‐Schmidt, Markus Zumbansen

2020Ophthalmology62 citationsDOI

Topics & Concepts

MedicineMacular degenerationReceiver operating characteristicPopulationCohortDrusenLasso (programming language)Internal medicineArtificial intelligenceOphthalmologyComputer scienceWorld Wide WebEnvironmental healthRetinal Diseases and TreatmentsOphthalmology and Visual Impairment StudiesRetinal Imaging and Analysis
Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning | Litcius