Summary of Synthetic Patient-physician Dialogue Generation From Clinical Notes Using Llm, by Trisha Das et al.
Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM
by Trisha Das, Dina Albassam, Jimeng Sun
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents SynDial, a novel approach for generating synthetic medical dialogue systems (MDS) from publicly available clinical notes. The goal is to provide realistic data for training MDS while ensuring patient privacy. SynDial uses a single large language model iteratively with zero-shot prompting and a feedback loop to generate high-quality dialogues. The feedback process evaluates the generated dialogues based on similarity and extractiveness, refining them until they meet predefined thresholds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic medical dialogue systems can enhance patient-physician communication, improve healthcare accessibility, and reduce costs. To create these systems, researchers need suitable data to train them. However, using real conversations is not possible due to privacy concerns. SynDial offers a solution by generating realistic synthetic dialogues from clinical notes. This approach uses an iterative process with feedback to ensure the generated dialogues are high-quality. |
Keywords
» Artificial intelligence » Large language model » Prompting » Zero shot
