|
Descripción
|
|
|---|---|
| Machine learning predictive models for breast cancer survival can improve if they are made specific to the stage of the cancer at the time of diagnosis. However, the relevance of the clinical parameters in that prediction, and the predictive quality of these models may change over time. Objective:To determine whether the findings on the influence of clinical parameters and the performance ofmachine learning models in the prediction of breast cancer survival have to be considered temporary or per-manent, and if temporary what is the period of validity of the new generated knowledge. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
International Journal of Medical Informatics |
|
ISSN
|
1386-5056 |
|
Factor de impacto JCR
|
|
|
Información de impacto
|
|
|
Volumen
|
129 |
|
DOI
|
10.1016 |
|
Número de revista
|
|
|
Desde la página
|
303 |
|
Hasta la página
|
311 |
|
Mes
|
SIN MES |
|
Ranking
|
|