Summary of A Text-to-tabular Approach to Generate Synthetic Patient Data Using Llms, by Margaux Tornqvist et al.
A text-to-tabular approach to generate synthetic patient data using LLMs
by Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach generates synthetic tabular patient data without requiring access to the original data, leveraging large language models (LLMs) to learn medical knowledge and generate realistic patient data in a low-resource setting. This method uses prior medical knowledge and in-context learning capabilities of LLMs to produce patient data that preserves clinical correlations, evaluated using fidelity, privacy, and utility metrics. The approach is easy to use and does not require original data or advanced machine learning skills, making it valuable for quickly generating custom-designed patient data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us get better healthcare by creating fake patient data that doctors can use without breaking patient privacy rules. Right now, getting this kind of data is hard because patients’ information is private and sharing it costs a lot. But scientists are working on making fake patient data that’s realistic and useful. This new method uses super smart computers called large language models to learn medical stuff and create fake patient data. It’s easy to use and doesn’t require special computer skills or access to real patient data. |
Keywords
* Artificial intelligence * Machine learning
