Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD).

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Zoli, Matteo;Guaraldi, Federica;Friso, Filippo;Rustici, Arianna;Asioli, Sofia;Pasquini, Ernesto;Serra, Carlo;Mazzatenta, Diego
2020

Abstract

Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD).
Zoli, Matteo; Staartjes, Victor E; Guaraldi, Federica; Friso, Filippo; Rustici, Arianna; Asioli, Sofia; Sollini, Giacomo; Pasquini, Ernesto; Regli, Luca; Serra, Carlo; Mazzatenta, Diego
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/760951
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