Determining an Optimal Oxygen Saturation Target Range Based on Neonatal Maturity: Demonstration of a Decision Tree Analytic.

Bachman, Thomas E; Iyer, Narayan P; Newth, Christopher J L; LeMoyne, Robert

Bachman, Thomas E; Iyer, Narayan P; Newth, Christopher J L; LeMoyne, Robert.

Afiliación

Bachman TE; Faculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.
Iyer NP; Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA 90027, USA.
Newth CJL; Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA 90027, USA.
LeMoyne R; Faculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.

Diagnostics (Basel) ; 13(21)2023 Oct 26.

Article en En | MEDLINE | ID: mdl-37958208

Palabras clave

decision tree classification; hyperoxemic-risk; hypoxemic-risk; machine learning; neonatal; oxygen saturation targeting

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Diagnostics (Basel) Año: 2023 Tipo del documento: Article País de afiliación: República Checa Pais de publicación: Suiza

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