Summary of Generating Survival Interpretable Trajectories and Data, by Andrei V. Konstantinov et al.
Generating Survival Interpretable Trajectories and Data
by Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model, an autoencoder with a specific structure, addresses three tasks: predicting survival trajectories, generating additional data, and creating counterfactual explanations. It uses the Beran estimator to provide predictions and determines censored indicators by solving a classification task. The model’s robustness during training and inference is ensured through a weighting scheme incorporated into the variational autoencoder. Numerical experiments on synthetic and real datasets demonstrate its efficiency and properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new model can help us predict when things will happen, like how long someone might survive with a certain treatment. It also generates extra data to supplement what we already have, which is helpful for making predictions. Additionally, it creates “what if” scenarios that show how different factors could affect the outcome of an event. The model is reliable and can be used in various settings. |
Keywords
* Artificial intelligence * Autoencoder * Classification * Inference * Variational autoencoder
