XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke

An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors—age, fasting glucose, and National Institutes of Health Stroke...

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Detalles Bibliográficos
Autores principales: Chung, Chen-Chih, Su, Emily Chia-Yu, Chen, Jia-Hung, Chen, Yi-Tui, Kuo, Chao-Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000880/
https://www.ncbi.nlm.nih.gov/pubmed/36899986
http://dx.doi.org/10.3390/diagnostics13050842

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000880/
https://www.ncbi.nlm.nih.gov/pubmed/36899986
http://dx.doi.org/10.3390/diagnostics13050842

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