Summary of Cehr-gpt: Generating Electronic Health Records with Chronological Patient Timelines, by Chao Pang et al.
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
by Chao Pang, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve, Krishna S. Kalluri, Elise L. Minto, Jason Patterson, Linying Zhang, George Hripcsak, Gamze Gürsoy, Noémie Elhadad, Karthik Natarajan
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 proposes a novel approach to generating synthetic Electronic Health Records (EHR) that preserves temporal dependencies in patient histories. By leveraging Generative Pre-trained Transformers (GPT) and a specific patient representation derived from CEHR-BERT, the authors demonstrate the ability to generate realistic EHR sequences. This has significant implications for applications such as disease progression analysis, population estimation, counterfactual reasoning, and synthetic data generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making fake health records that are really similar to real ones. Right now, researchers can’t always get access to real health data, so they use fake data instead. The problem with this fake data is that it’s usually not very good at showing what happens over time. This new way of making fake data uses a special kind of computer program called GPT. It helps make the fake data better by including things like when people got sick or took medicine. This could be really helpful for scientists trying to understand how diseases work and for making predictions about health. |
Keywords
* Artificial intelligence * Bert * Gpt * Synthetic data
