Summary of Generating Synthetic Electronic Health Record (ehr) Data: a Review with Benchmarking, by Xingran Chen et al.
Generating Synthetic Electronic Health Record (EHR) Data: A Review with Benchmarking
by Xingran Chen, Zhenke Wu, Xu Shi, Hyunghoon Cho, Bhramar Mukherjee
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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 We conduct a scoping review of existing approaches for generating synthetic Electronic Health Record (EHR) data and provide open-source software to help practitioners choose suitable methods. Our search yields 42 studies classified into five categories, with seven implemented methods benchmarked on MIMIC-III and MIMIC-IV datasets. Evaluation metrics include data fidelity, downstream utility, privacy protection, and computational cost. We find that GAN-based methods excel in preserving fidelity and utility on MIMIC-III, while rule-based methods prioritize privacy protection. Our Python package, SynthEHRella'', integrates various approaches and evaluation metrics to facilitate method selection. A decision tree guides the choice among benchmarked methods, suggesting GAN-based methods for distributional shifts and CorGAN/MedGAN for association/predictive modeling.</td> </tr> <tr> <td>Low</td> <td>GrooveSquid.com (original content)</td> <td><strong>Low Difficulty Summary</strong><br>We looked at many ways to make fake medical data that looks like real Electronic Health Records (EHRs). We wanted to help people decide which method is best. We searched through lots of research papers, found 42 studies, and grouped them into five categories. Then, we tested seven methods on two big datasets. We checked how well each method worked by looking at things like how much it looked like real data, how useful it was for other tasks, and how private it kept the information. We also made a special tool calledSynthEHRella’’ that helps people choose which method to use based on what they need. Our results show that some methods are better than others depending on what you’re trying to do. |
Keywords
» Artificial intelligence » Decision tree » Gan
