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

TL;DR

This paper presents SurvTimeSurvival, a Transformer-based model that improves survival time prediction for patients with multiple visits by handling time-varying data and addressing data sparsity, outperforming existing methods.

Contribution

The study introduces a novel Transformer-based survival analysis model that incorporates synthetic data generation to handle data sparsity and effectively models multiple patient visits.

Findings

Outperforms state-of-the-art deep learning methods on survival datasets.

Effectively models time-varying covariates and multiple patient visits.

Enhances survival prediction accuracy for complex medical data.

Abstract

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.