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
Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression
The proposed method is a semiparametric approach to AFT modeling that does not impose any distributional assumptions on the survival time distribution.
Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration
dviraran/strafe
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Deep-learning techniques, particularly the transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records.
Using Geographic Location-based Public Health Features in Survival Analysis
Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features.
Interpretable machine learning for time-to-event prediction in medicine and healthcare
Time-to-event prediction, e. g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications.
A copula-based boosting model for time-to-event prediction with dependent censoring
A characteristic feature of time-to-event data analysis is possible censoring of the event time.
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
chiragnagpal/DeepSurvivalMachines
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Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
ZhiliangWu/DKAFT
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Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data.
A Deep Variational Approach to Clustering Survival Data
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features.
