Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database.

Cao, Yang; Forssten, Maximilian Peter; Sarani, Babak; Montgomery, Scott; Mohseni, Shahin

Cao, Yang; Forssten, Maximilian Peter; Sarani, Babak; Montgomery, Scott; Mohseni, Shahin.

Afiliación

Cao Y; Clinical Epidemiology and Biostatistics, School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 701 82 Orebro, Sweden.
Forssten MP; Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden.
Sarani B; Department of Orthopedic Surgery, Örebro University Hospital, 701 85 Orebro, Sweden.
Montgomery S; School of Medical Sciences, Örebro University, 701 82 Orebro, Sweden.
Mohseni S; Center of Trauma and Critical Care, George Washington University, Washington, DC 20037, USA.

J Pers Med ; 13(9)2023 Sep 19.

Article en En | MEDLINE | ID: mdl-37763168

Palabras clave

Trauma Quality Improvement Program (TQIP); extreme gradient boosting (XGBoost); machine learning; prediction model; survival analysis; traumatic brain injury

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Pers Med Año: 2023 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Suiza

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google