Research Paper#Survival Analysis, Deep Learning, Time-dependent Exposures🔬 ResearchAnalyzed: Jan 3, 2026 16:14
Deep Learning for Cumulative Effects in Survival Analysis
Published:Dec 29, 2025 00:22
•1 min read
•ArXiv
Analysis
This paper introduces CENNSurv, a novel deep learning approach to model cumulative effects of time-dependent exposures on survival outcomes. It addresses limitations of existing methods, such as the need for repeated data transformation in spline-based methods and the lack of interpretability in some neural network approaches. The paper highlights the ability of CENNSurv to capture complex temporal patterns and provides interpretable insights, making it a valuable tool for researchers studying cumulative effects.
Key Takeaways
- •Introduces CENNSurv, a deep learning approach for survival analysis.
- •Addresses limitations of existing methods in terms of data transformation and interpretability.
- •Demonstrates the ability to model complex temporal patterns and provide interpretable insights.
- •Evaluated on real-world datasets, showing practical applications.
Reference
“CENNSurv revealed a multi-year lagged association between chronic environmental exposure and a critical survival outcome, as well as a critical short-term behavioral shift prior to subscription lapse.”