title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð²Ð°ÑÐ¸Ð°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð²ÑÐ¾ÐºÐ¾Ð´ÐµÑÐ° Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÐ¶Ð¸Ð²Ð°ÐµÐ¼Ð¾ÑÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Research and development of a generative machine learning model based on variational autoencoder in terms of the survival analysis
Creators: name: ÐÐ¸ÑÐ¿Ð¸ÑÐµÐ½ÐºÐ¾, Ð¡ÑÐ°Ð½Ð¸ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
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Ð°Ð½Ð°Ð»Ð¸Ð· Ð²ÑÐ¶Ð¸Ð²Ð°ÐµÐ¼Ð¾ÑÑÐ¸
Ð¾ÑÐµÐ½ÐºÐ° ÐÐµÑÐ°Ð½Ð°
data generation
variational autoencoder
survival analysis
Beran estimator

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description: A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks: 1. 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. 2. Generation of the additional data based on a given training set that would supplement the original dataset. The feature vectors are provided by the variational autoencoder, the expected time to event by the Beran estimator and the censoring labels are generated using a third party classifier. 3. Generation of 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. This paper demonstrates the efficiency and properties of the developed model using numerical experiments on synthetic and real datasets.