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.

利用Transformer模型和合成数据生成，本研究介绍了“SurvTimeSurvival：对多次访问/记录的患者进行生存分析”，在处理时间变化的协变量和协变量数据的复杂性方面取得了显著进展，解决了生存分析数据中常见的数据稀疏性问题，并展示了我们的方法在协变量和时间变化的协变量数据集上的优越性能，旨在提高对各种医疗状况下个体患者生存轨迹的理解，从而提高预测准确性，并在临床试验的设计和新疗法的创立中发挥关键作用。

多次门诊/记录患者的生存分析