Deep Clustering with Variational Autoencoder

In this article, we explore the application of Neural Networks (NN) in Survival Analysis (SA), specifically focusing on their integration with traditional models like Cox-PH. We delve into the historical background of NN applications in SA, beginning from simple feed-forward networks introduced in 1995 to more complex architectures like DeepSurv and Bayesian networks. Our analysis highlights the advantages of combining NNs with Cox-PH models, improving prediction accuracy and interpretability.

NNs offer several benefits when applied to SA

1. Non-linearity: Traditional Cox-PH models assume a linear relationship between predictors and the survival outcome. However, this assumption often doesn’t hold true in real-world scenarios, where interactions and non-linear effects play a significant role. NNs can capture these complex relationships by learning non-linear patterns in the data.
2. Interpretable results: Cox-PH models provide interpretable results by highlighting the contribution of each predictor to the hazard ratio. However, these interpretations can be limited when dealing with complex models or large datasets. NNs can help address this challenge by providing more transparent and comprehensible results through techniques like feature importance and partial dependence plots.
3. Improved prediction: By combining NNs with Cox-PH models, we can leverage the strengths of both approaches to achieve better prediction accuracy. This is particularly useful when dealing with small sample sizes or limited data availability, where traditional models may struggle to provide accurate predictions.
4. Comparison with other methods: We compare the performance of NNs integrated with Cox-PH models to other related approaches, demonstrating their competitiveness in terms of prediction accuracy and interpretability.
5. Future directions: The integration of NNs with traditional SA models opens up new avenues for research and application development. Future work may focus on improving the efficiency and scalability of these methods or exploring alternative architectures and techniques.
   In summary, this article demonstrates the potential of Neural Networks in Survival Analysis by integrating them with traditional Cox-PH models. By leveraging NNs’ ability to capture non-linear relationships and provide interpretable results, we can improve prediction accuracy and interpretability in various SA scenarios.