Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis

In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural n...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Bennis, Achraf, Mouysset, Sandrine, Serrurier, Mathieu
Format: Article
Language:English
Subjects:
Censored data (mathematics)
Data analysis
Datasets
Mixtures
Neural networks
Parameter estimation
Predictive maintenance
Survival
Synthetic data
Weibull distribution
Online Access:Get full text
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Summary:In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture and the associated loss functions that takes into account the right-censored data. We extend the approach to a finite mixture of two-parameter Weibull distributions. We first validate that our model is able to precisely estimate the right parameters of the conditional Weibull distribution on synthetic datasets. In numerical experiments on two real-word datasets (METABRIC and SEER), our model outperforms the state-of-the-art methods. We also demonstrate that our approach can consider any survival time horizon.
ISSN:2331-8422