SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records

2023-11-16 · Hung Le, Ong Eng-Jon, Bober Miroslaw

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.

📄 PDF Abstract BibTeX arXiv:2311.09854

Code (1)

davidlee1102/surtimesurvival 공식 구현 pytorch

Tasks

Survival AnalysisSynthetic Data Generation

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