Summary of Keeping Experts in the Loop: Expert-guided Optimization For Clinical Data Classification Using Large Language Models, by Nader Karayanni et al.
Keeping Experts in the Loop: Expert-Guided Optimization for Clinical Data Classification using Large Language Models
by Nader Karayanni, Aya Awwad, Chein-Lien Hsiao, Surish P Shanmugam
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach is proposed to leverage Large Language Models (LLMs) for extracting insights from unstructured clinical notes in healthcare, addressing the challenge of prompt engineering. The framework, called StructEase, integrates human expertise with automatic prompt optimization, minimizing expert intervention while enhancing classification outcomes. A key component is SamplEase, an iterative sampling algorithm that identifies high-value cases where expert feedback drives significant performance improvements. This targeted approach reduces labeling redundancy and error, achieving notable gains in F1 score. The framework’s performance is evaluated using a dataset of de-identified clinical narratives from the US National Electronic Injury Surveillance System (NEISS), demonstrating significant improvements compared to current methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to use Large Language Models (LLMs) in healthcare is being developed. Right now, it’s hard to get LLMs to understand unstructured medical notes because of how we ask them questions. This problem needs a solution. The solution proposed here is called StructEase. It combines human expertise with automatic ways to improve question-asking. This helps reduce the amount of time and effort needed from experts while still getting good results. A key part of this approach is an algorithm that finds important cases where expert feedback makes a big difference. By doing things in a targeted way, we can reduce mistakes and get better results. The new method was tested on real medical records and showed significant improvements over current methods. |
Keywords
» Artificial intelligence » Classification » F1 score » Optimization » Prompt
