Abstract
Background and objective:Recently, the rise of models based on deep neural networks (DNNs) has demonstrated enhanced effectiveness in survival analysis. However, deep learning dealing with survival analysis usually required specific architecture or strict discretization scheme limiting the temporal precision of input and output values. Methods:This research introduces the Implicit Continuous-Time Survival Function (ICTSurF), built on a continuous-time survival model, and constructs survival distribution through implicit representation. As a result, our method is capable of accepting inputs in continuous-time space and producing survival probabilities in continuous-time space, independent of neural network architecture. Results:Comparative evaluations against existing methods underscore the remarkable competitiveness of our proposed approach. Furthermore, we highlight the advantages of a flexible discretization scheme, showing improved performance with a lower number of discretizations compared to a rigid scheme. Conclusion:Our model exhibits competitive performance compared to existing methods, enhancing the flexibility of input data.
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