SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records
This work addresses survival prediction for patients with multiple medical visits, which could improve clinical trial design and treatment development, though it appears incremental as it adapts existing models to a specific domain.
This study tackled the challenge of accurately predicting survival times for patients with severe diseases by introducing SurvTimeSurvival, a Transformer-based model that handles time-varying and static covariates while addressing data sparsity through synthetic data generation. The method outperformed state-of-the-art deep learning approaches on both covariates and time-varying covariates datasets.
The accurate prediction of survival times for patients with severe diseases remains a critical challenge despite recent advances in artificial intelligence. This study introduces "SurvTimeSurvival: Survival Analysis On Patients With Multiple Visits/Records", utilizing the Transformer model to not only handle the complexities of time-varying covariates but also covariates data. We also tackle the data sparsity issue common to survival analysis datasets by integrating synthetic data generation into the learning process of our model. We show that our method outperforms state-of-the-art deep learning approaches on both covariates and time-varying covariates datasets. Our approach aims not only to enhance the understanding of individual patient survival trajectories across various medical conditions, thereby improving prediction accuracy, but also to play a pivotal role in designing clinical trials and creating new treatments.