Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality.

Andonov, D I; Ulm, B; Graessner, M; Podtschaske, A; Blobner, M; Jungwirth, B; Kagerbauer, S M

Andonov, D I; Ulm, B; Graessner, M; Podtschaske, A; Blobner, M; Jungwirth, B; Kagerbauer, S M.

Afiliação

Andonov DI; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
Ulm B; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
Graessner M; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.
Podtschaske A; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.
Blobner M; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
Jungwirth B; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
Kagerbauer SM; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, University of Ulm, Albert-Einstein-Allee 23, Ulm, 89081, Germany.

BMC Med Inform Decis Mak ; 23(1): 67, 2023 04 12.

Article em En | MEDLINE | ID: mdl-37046259

Assuntos

COVID-19; Humanos; Pandemias; Algoritmos; Redes Neurais de Computação; Aprendizado de Máquina

Palavras-chave

Covariate shift; Covid-19; Machine learning; Model degradation

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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