A deep survival analysis method based on ranking.
Artif Intell Med
; 98: 1-9, 2019 07.
Article
en En
| MEDLINE
| ID: mdl-31521247
ABSTRACT
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineering or prior knowledge to model at an individual level. Some survival analysis models can avoid these problems by using machine learning extended the CPH model, and higher performance has been reported. In this paper, we propose an innovative loss function that is defined as the sum of an extended mean squared error loss and a pairwise ranking loss based on ranking information on survival data. We apply this loss function to optimize a deep feed-forward neural network (RankDeepSurv), which can be used to model survival data. We demonstrate that the performance of our model, RankDeepSurv, is superior to that of other state-of-the-art survival models based on an analysis of 4 public medical clinical datasets. When modelling the prognosis of nasopharyngeal carcinoma (NPC), RankDeepSurv achieved better prognostic accuracy than the CPH established by clinical experts. The difference between high and low risk groups in the RankDeepSurv model is greater than the difference in the CPH. The results show that our method has considerable potential to model survival data in medical settings.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Pronóstico
/
Modelos de Riesgos Proporcionales
/
Análisis de Supervivencia
/
Neoplasias Nasofaríngeas
/
Carcinoma Nasofaríngeo
/
Aprendizaje Profundo
/
Recurrencia Local de Neoplasia
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
China