Summary of Generating Synthetic Free-text Medical Records with Low Re-identification Risk Using Masked Language Modeling, by Samuel Belkadi et al.
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling
by Samuel Belkadi, Libo Ren, Nicolo Micheletti, Lifeng Han, Goran Nenadic
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper proposes a novel system for generating synthetic free-text medical records using Masked Language Modeling, addressing the challenge of preserving patient privacy while introducing diversity in generations. The system, consisting of approximately 120M parameters, minimizes inference cost and achieves high-quality synthetic data with a HIPAA-compliant PHI recall rate of 96% and re-identification risk of 3.5%. Downstream evaluations demonstrate that the generated data can effectively train models with performance comparable to real data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it possible to use medical records for research without breaking patient privacy rules. Right now, medical records are only available internally because they’re too sensitive. The researchers found a way to make fake versions of these records using special language modeling techniques. This lets them control how diverse the fake records are and keep patients’ information safe. They tested their method and it worked well: the fake records were very accurate and didn’t give away any identifying patient information. |
Keywords
» Artificial intelligence » Inference » Recall » Synthetic data
