The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists.
Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners / Uncini, Antonino; Aretusi, Graziano; Manganelli, Fiore; Sekiguchi, Yukari; Magy, Laurent; Tozza, Stefano; Tsuneyama, Atsuko; Lefour, Sophie; Kuwabara, Satoshi; Santoro, Lucio; Ippoliti, Luigi. - In: NEUROLOGICAL SCIENCES. - ISSN 1590-1874. - (2020). [10.1007/s10072-020-04499-y]
Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners
Manganelli, Fiore;Tozza, Stefano;Santoro, Lucio;
2020
Abstract
The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists.| File | Dimensione | Formato | |
|---|---|---|---|
|
s10072-020-04499-y.pdf
accesso aperto
Licenza:
Copyright dell'editore
Dimensione
788.9 kB
Formato
Adobe PDF
|
788.9 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
