Time-to-Event Prediction
13 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Time-to-Event Prediction
GBSG2
Latest papers with no code
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth
We evaluated our method on multiple clinically-relevant endpoints, including time to intracranial progression (ICP), progression-free survival (PFS) after SRS, overall survival (OS), and time to ICP and/or death (ICPD), on a variety of both statistical and non-statistical models, including CoxPH, conditional survival forest (CSF), and neural multi-task linear regression (NMTLR).
Towards simple time-to-event modeling: optimizing neural networks via rank regression
Time-to-event analysis, also known as survival analysis, aims to predict the first occurred event time, conditional on a set of features.
Simulating time to event prediction with spatiotemporal echocardiography deep learning
Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period.
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
As an alternative, we present an interpretable neural network approach to jointly learn a survival model to predict time-to-event outcomes while simultaneously learning how features relate in terms of a topic model.
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches
The two approaches we propose differ in the generality of topic models they can learn.
Prediction of New Onset Diabetes after Liver Transplant
Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year.
Neural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains.
