time-series-5

Time-to-Event Prediction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks

A. Dubrawski, Chirag Nagpal, Xinyu Li•Sun Mar 01 2020

A new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner that does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model.

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Interpretable machine learning for time-to-event prediction in medicine and healthcare

Hubert Baniecki, P. Biecek, Bartlomiej Sobieski, Przemyslaw Bombi'nski, Patryk Szatkowski•Thu Mar 16 2023

It is shown how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel multi-modal dataset created from 1235 X-ray images with textual radiology reports annotated by human experts.

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Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning

Wentao Bao, Qi Yu, Yu Kong•Fri Jul 31 2020

An uncertainty-based accident anticipation model with spatio-temporal relational learning that sequentially predicts the probability of traffic accident occurrence with dashcam videos is proposed to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations.

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auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

A. Dubrawski, Chirag Nagpal, Willa Potosnak•Thu Apr 14 2022

The ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions is demonstrated, through real world case studies employing a large subset of the SEER oncology incidence data.

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Time-to-Event Prediction with Neural Networks and Cox Regression

Håvard Kvamme, Ørnulf Borgan, Ida Scheel•Sun Jun 30 2019

New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks, and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood.

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Variational learning of individual survival distributions

Ricardo Henao, Chenyang Tao, Zidi Xiu•Sun Mar 08 2020

A variational time-to-event prediction model, named Variational Survival Inference (VSI), is introduced, which builds upon recent advances in distribution learning techniques and deep neural networks to address the challenges of non-parametric distribution estimation.

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A Deep Variational Approach to Clustering Survival Data

Ricards Marcinkevics, Julia E. Vogt, Laura Manduchi, M. Massi, V. Gotta, Timothy Müller, F. Vasella, M. Neidert, M. Pfister•Wed Jun 09 2021

A novel semi-supervised probabilistic approach to cluster survival data is introduced by leveraging recent advances in stochastic gradient variational inference and employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times.

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Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models

Volker Tresp, Zhiliang Wu, Yinchong Yang, P. Fasching•Sun Jul 25 2021

This work proposes Deep Kernel Accelerated Failure Time models for the time-to-event prediction task, enabling uncertainty-awareness of the prediction by a pipeline of a recurrent neural network and a sparse Gaussian Process.

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SurvSHAP(t): Time-dependent explanations of machine learning survival models

Hubert Baniecki, P. Biecek, Mateusz Krzyzi'nski, Mikolaj Spytek•Mon Aug 22 2022

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.

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A copula-based boosting model for time-to-event prediction with dependent censoring

A. Midtfjord, R. D. Bin, A. Huseby•Sun Oct 09 2022

Clayton-boost is introduced, a boosting approach built upon the accelerated failure time model, which uses a Clayton copula to handle the dependency between the event and censoring distributions, and shows a strong ability to remove prediction bias at the presence of dependent censoring.

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