Machine learning based prediction of length of stay in acute ischaemic stroke of the anterior circulation in patients treated with thrombectomy.

Feyen, Ludger; Pinz-Bogesits, Jan; Blockhaus, Christian; Katoh, Marcus; Haage, Patrick; Nitsch, Louisa; Schaub, Christina

Feyen, Ludger; Pinz-Bogesits, Jan; Blockhaus, Christian; Katoh, Marcus; Haage, Patrick; Nitsch, Louisa; Schaub, Christina.

Afiliação

Feyen L; Department of Diagnostic and Interventional Radiology, Helios Klinikum Krefeld, Krefeld, Germany.
Pinz-Bogesits J; Faculty of Health, University Witten/Herdecke, School of Medicine, Witten, Germany.
Blockhaus C; Department of Diagnostic and Interventional Radiology, Helios Klinikum Wuppertal, Wuppertal, Germany.
Katoh M; Department of Diagnostic and Interventional Radiology, Helios Klinikum Krefeld, Krefeld, Germany.
Haage P; Faculty of Health, University Witten/Herdecke, School of Medicine, Witten, Germany.
Nitsch L; Department of Cardiology, Heart Centre Niederrhein, Helios Clinic Krefeld, Krefeld, Germany.
Schaub C; Department of Diagnostic and Interventional Radiology, Helios Klinikum Krefeld, Krefeld, Germany.

Interv Neuroradiol ; : 15910199231197615, 2023 Sep 06.

Article em En | MEDLINE | ID: mdl-37671446

Palavras-chave

Acute ischaemic stroke; length of stay; machine learning; thrombectomy

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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