[Random survival forest: applying machine learning algorithm in survival analysis of biomedical data].
Zhonghua Yu Fang Yi Xue Za Zhi
; 55(1): 104-109, 2021 Jan 06.
Article
en Zh
| MEDLINE
| ID: mdl-33455140
ABSTRACT
Traditional survival methods have a wide application in the field of biomedical research. However, applying traditional survival methods requires data to meet a set of special assumptions while the Random Survival Forest model can overcome this inconvenience. Herein, we used the clinical data of Primary Biliary Cholangitis (PBC) from Mayo Clinic to introduce and demonstrate Random Survival Forest model from mathematical principles, model building, practical example and attentions, aiming to provide a novel method for doing survival analysis.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Aprendizaje Automático
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Límite:
Humans
Idioma:
Zh
Año:
2021
Tipo del documento:
Article