Time-to-Event Prediction
14 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Time-to-Event Prediction
GBSG2
Most implemented papers
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
autonlab/DeepSurvivalMachines
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We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
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.
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.
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.
Time-to-Event Prediction with Neural Networks and Cox Regression
havakv/pycox
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New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks.
Variational Learning of Individual Survival Distributions
ZidiXiu/VSI
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The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making.
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships.
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-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.
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.
