In collaboration with University College London (UCL) and support by funding from OSIC, the development of CenTime represents a significant leap forward in survival analysis, particularly in advancing respiratory health. CenTime, which stands for “Event-Conditional Modeling of Censoring in Survival Analysis” (a name derived from its focus on censoring and time in survival analysis), is a pioneering effort to overcome critical limitations in existing methodologies, combining insights from respiratory medical professionals and data scientists.

Addressing Limitations in Survival Analysis
Traditional approaches to survival analysis often fall short inaccurately estimating event times, crucial for predicting outcomes such as mortality or disease recurrence. CenTime confronts these limitations head-on with several advancements:

1. 1. 1. Direct Estimate of Event Time Unlike conventional methods that primarily rank patients by survivability, CenTime directly estimates the time to event occurrence, providing a more detailed understanding of disease progression.
      2. Innovative  Censoring Mechanism CenTime leverages a novel event-conditional censoring mechanism, ensuring robust performance even with limited uncensored data across diverse datasets.
      3. Ordinal Nature of Event Time Encoding By capturing the ordinal nature of event time, CenTime acknowledges the chronological progression of events inherent in survival analysis.
      4. Robust Performance CenTime demonstrates reliable performance even with scarce uncensored data, making it dependable tool for researchers and clinicians.

Integration with Deep Learning, Performance Evaluation
In comparative evaluations against standard survival analysis models such as the Cox proportional-hazard model and DeepHit, CenTime has consistently demonstrated state-of-the-art performance. Notably, it excels in predicting time-to-death while maintaining comparable ranking performance, validating its utility across diverse clinical scenarios.