Released Open-Source
CenTime
Event-conditional modeling of
censoring in survival analysis
censoring in survival analysis
In collaboration with University College London (UCL) and supported 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:
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.
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:
- Direct Estimation 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. - Innovative Censoring Mechanism
CenTime leverages a novel event-conditional censoring mechanism, ensuring robust performance even with limited uncensored data across diverse datasets. - 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. - Robust Performance
CenTime demonstrates reliable performance even with scarce uncensored data, making it a 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.
Watch Ahmed Shahin present the CenTime model
Gain insights into the development and implications of CenTime directly from one of its creators.
Additional Resources
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Read the Published Paper
Delve into the technical intricacies of CenTime through its peer-reviewed publication in the National Library of Medicine's Center for Biotechnology Information. |
Access the Open-source Code
Explore the implementation details of CenTime and actively contribute to its development via the open-source code repository on GitHub. |