A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.
J Cardiovasc Transl Res
; 2024 Aug 05.
Article
em En
| MEDLINE
| ID: mdl-39103715
ABSTRACT
Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models CosAttnSurv and CosAttnSurv + DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv + DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Cardiovasc Transl Res
Assunto da revista:
ANGIOLOGIA
/
CARDIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Índia
País de publicação:
Estados Unidos