Cargando…
The roles of predictors in cardiovascular risk models - a question of modeling culture?
BACKGROUND: While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demons...
Autores principales: | Wallisch, Christine, Agibetov, Asan, Dunkler, Daniela, Haller, Maria, Samwald, Matthias, Dorffner, Georg, Heinze, Georg |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684157/ https://www.ncbi.nlm.nih.gov/pubmed/34922459 http://dx.doi.org/10.1186/s12874-021-01487-4 |
Ejemplares similares
-
Variable selection – A review and recommendations for the practicing statistician
por: Heinze, Georg, et al.
Publicado: (2018) -
Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resampling
por: Wallisch, Christine, et al.
Publicado: (2020) -
Fast and scalable neural embedding models for biomedical sentence classification
por: Agibetov, Asan, et al.
Publicado: (2018) -
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
por: Blagec, Kathrin, et al.
Publicado: (2019) -
Re-estimation improved the performance of two Framingham cardiovascular risk equations and the Pooled Cohort equations: A nationwide registry analysis
por: Wallisch, Christine, et al.
Publicado: (2020)