Generating Survival Interpretable Trajectories and Data
Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin

TL;DR
This paper introduces a novel autoencoder-based model that generates survival data, predicts event times, and creates interpretable trajectories, offering a new way to understand and augment survival analysis with counterfactual insights.
Contribution
It presents a new autoencoder structure that predicts survival outcomes, generates additional data, and produces interpretable, time-dependent trajectories for counterfactual analysis.
Findings
Model is robust during training and inference.
Effective on synthetic and real datasets.
Provides interpretable trajectories for survival analysis.
Abstract
A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks. First, it provides predictions in the form of the expected event time and the survival function for a new generated feature vector on the basis of the Beran estimator. Second, the model generates additional data based on a given training set that would supplement the original dataset. Third, the most important, it generates a prototype time-dependent trajectory for an object, which characterizes how features of the object could be changed to achieve a different time to an event. The trajectory can be viewed as a type of the counterfactual explanation. The proposed model is robust during training and inference due to a specific weighting scheme incorporating into the variational autoencoder. The model also determines the censored…
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Taxonomy
TopicsStatistical Methods and Inference · Machine Learning in Healthcare · Statistical Methods and Bayesian Inference
MethodsSparse Evolutionary Training
