CenTime: Event-Conditional Modelling of Censoring in Survival Analysis

by   Ahmed H. Shahin, et al.

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Furthermore, the effective utilization of censored samples - training data points where the exact event time is unknown - is essential for improving the predictive accuracy of the model. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach with standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.


page 1

page 2

page 3

page 4


Using Geographic Location-based Public Health Features in Survival Analysis

Time elapsed till an event of interest is often modeled using the surviv...

Survival and Neural Models for Private Equity Exit Prediction

Within the Private Equity (PE) market, the event of a private company un...

A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer

Survival analysis has been developed and applied in the number of areas ...

The Concordance Index decomposition: a measure for a deeper understanding of survival prediction models

The Concordance Index (C-index) is a commonly used metric in Survival An...

Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of ...

DeepCENT: Prediction of Censored Event Time via Deep Learning

With the rapid advances of deep learning, many computational methods hav...

Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality

Utilizing clinical texts in survival analysis is difficult because they ...

Please sign up or login with your details

Forgot password? Click here to reset