Summary of Zero-shot and Few-shot Generation Strategies For Artificial Clinical Records, by Erlend Frayling et al.
Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
by Erlend Frayling, Jake Lever, Graham McDonald
First submitted to arxiv on: 13 Mar 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 This innovative study tackles the challenge of accessing historical patient data for clinical research while ensuring individual privacy is protected. By creating synthetic medical records that mirror real patient data, researchers can bypass this obstacle and gain valuable insights without compromising sensitive information. Specifically, the study assesses the capability of Large Language Models (LLMs) to create accurate synthetic medical records using zero-shot and few-shot prompting strategies, as well as fine-tuned methodologies that require sensitive patient data during training. The focus is on generating synthetic narratives for the History of Present Illness section using the MIMIC-IV dataset for comparison. The study introduces a novel prompting technique called chain-of-thought approach, which enhances the model’s ability to generate more accurate and contextually relevant medical narratives without prior fine-tuning. The findings suggest that this prompted approach allows the zero-shot model to achieve results comparable to those of fine-tuned models, as evaluated by Rouge metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps solve a big problem in medicine: how to get access to old patient data for research while keeping patients’ privacy safe. One way to do this is by creating fake medical records that are similar to real ones, but don’t have any actual patient information. This can be tricky, especially when training special types of computer models called Large Language Models (LLMs). The researchers tested these LLMs using different ways to prompt them, like zero-shot and few-shot prompting strategies, and even fine-tuned models that need sensitive patient data during training. They focused on creating fake medical histories for patients, using a big dataset called MIMIC-IV as a reference. The study introduces a new way of giving these prompts called the chain-of-thought approach, which helps the LLMs create more accurate and relevant medical stories without needing fine-tuning. Overall, this approach lets the zero-shot model achieve results similar to those of fine-tuned models, according to special metrics. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Prompt * Prompting * Rouge * Zero shot
