Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.

Sonnenschein, Kristina; Stojanovic, Stevan D; Dickel, Nicholas; Fiedler, Jan; Bauersachs, Johann; Thum, Thomas; Kunz, Meik; Tongers, Jörn

Sonnenschein, Kristina; Stojanovic, Stevan D; Dickel, Nicholas; Fiedler, Jan; Bauersachs, Johann; Thum, Thomas; Kunz, Meik; Tongers, Jörn.

Afiliación

Sonnenschein K; Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, 30625 Hannover, Germany.
Stojanovic SD; Department of Cardiology and Angiology, Hannover Medical School, 30625 Hannover, Germany.
Dickel N; Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, 30625 Hannover, Germany.
Fiedler J; Department of Cardiology and Angiology, Hannover Medical School, 30625 Hannover, Germany.
Bauersachs J; Chair of Medical Informatics, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, 91054 Erlangen, Germany.
Thum T; Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Hannover Medical School, 30625 Hannover, Germany.
Kunz M; Fraunhofer Institute of Toxicology and Experimental Medicine, 30625 Hannover, Germany.
Tongers J; Department of Cardiology and Angiology, Hannover Medical School, 30625 Hannover, Germany.

Biomedicines ; 9(10)2021 Oct 13.

Article en En | MEDLINE | ID: mdl-34680572

Palabras clave

Random Forest; artificial intelligence; classification model; machine learning; peripheral artery disease; vascular disease

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies Idioma: En Revista: Biomedicines Año: 2021 Tipo del documento: Article País de afiliación: Alemania

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google