Summary of Conditioning on Time Is All You Need For Synthetic Survival Data Generation, by Mohd Ashhad and Ricardo Henao
Conditioning on Time is All You Need for Synthetic Survival Data Generation
by Mohd Ashhad, Ricardo Henao
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper tackles the challenge of synthetic data generation in specialized applications like survival analysis, where censoring is a significant obstacle. Current methods struggle to accurately reproduce the event-time distributions for both observed and censored events. To overcome this issue, the authors propose a simple paradigm that generates covariates conditioned on event times and censoring indicators, leveraging existing conditional generative models for tabular data without additional computational overhead. The proposed method outperforms competitive baselines in generating survival data, leading to improved performance of downstream survival models trained on synthetic data and tested on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Survival analysis is a way to understand how people or things change over time. Right now, it’s hard to make fake data that looks like real data when dealing with these kinds of problems. That’s because some events don’t happen exactly when they’re supposed to (this is called censoring). Researchers want to create fake data that accurately shows when events might happen, but this is tricky. To solve this problem, scientists came up with a new way to make fake survival data by using existing tools and techniques. They tested their method on real-world data and it worked better than other approaches. |
Keywords
» Artificial intelligence » Synthetic data
