Summary of In-context Learning For Preserving Patient Privacy: a Framework For Synthesizing Realistic Patient Portal Messages, by Joseph Gatto et al.
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
by Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Sarah Masud Preum
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 abstract proposes an LLM-powered framework for generating realistic patient portal messages, aiming to optimize clinician workflows. The approach uses few-shot grounded text generation, requiring a small number of de-identified messages to train the models. Clinical experts deem this framework HIPAA-friendly, unlike existing approaches that cannot guarantee all sensitive attributes will be protected. The authors demonstrate the quality of their generated data through extensive quantitative and human evaluation, outperforming comparable methods and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to generate patient portal messages using machine learning models. This is important because healthcare providers are getting overwhelmed with messages from patients, which can lead to burnout. The proposed method requires only a few examples of real messages to create fake ones that look like the real thing. Experts checked the generated data and agreed it’s safe for use in medical settings. The paper shows that this approach produces better results than other methods and datasets. |
Keywords
» Artificial intelligence » Few shot » Machine learning » Text generation
