Cargando…
From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning
As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disabilit...
Autores principales: | Gkantzios, Aimilios, Kokkotis, Christos, Tsiptsios, Dimitrios, Moustakidis, Serafeim, Gkartzonika, Elena, Avramidis, Theodoros, Tripsianis, Gregory, Iliopoulos, Ioannis, Aggelousis, Nikolaos, Vadikolias, Konstantinos |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10532952/ https://www.ncbi.nlm.nih.gov/pubmed/37763143 http://dx.doi.org/10.3390/jpm13091375 |
Ejemplares similares
-
Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors’ Functional Outcome upon Discharge Utilizing Explainable Machine Learning
por: Gkantzios, Aimilios, et al.
Publicado: (2023) -
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
por: Kokkotis, Christos, et al.
Publicado: (2022) -
Copeptin Implementation on Stroke Prognosis
por: Karatzetzou, Stella, et al.
Publicado: (2023) -
Investigating the Predictive Value of Thyroid Hormone Levels for Stroke Prognosis
por: Gkantzios, Aimilios, et al.
Publicado: (2023) -
Exploring the Impact of Cerebral Microbleeds on Stroke Management
por: Sousanidou, Anastasia, et al.
Publicado: (2023)