Summary of Leveraging Prompt-learning For Structured Information Extraction From Crohn’s Disease Radiology Reports in a Low-resource Language, by Liam Hazan et al.
Leveraging Prompt-Learning for Structured Information Extraction from Crohn’s Disease Radiology Reports in a Low-Resource Language
by Liam Hazan, Gili Focht, Naama Gavrielov, Roi Reichart, Talar Hagopian, Mary-Louise C. Greer, Ruth Cytter Kuint, Dan Turner, Moti Freiman
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 discusses the challenges of converting free-text radiology reports into structured data using Natural Language Processing (NLP) techniques, particularly in less common languages like Hebrew. Generative large language models (LLMs) typically underperform with these languages and can pose risks to patient privacy. The study introduces SMP-BERT, a novel prompt learning method that leverages the structured nature of reports to overcome challenges like data imbalance. In a study involving 8,000 patients and 10,000 radiology reports in Hebrew, SMP-BERT significantly outperformed traditional fine-tuning methods in detecting infrequent conditions (AUC: 0.99 vs 0.94, F1: 0.84 vs 0.34). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to understand medical reports written in languages like Hebrew. It’s hard because language models aren’t very good with less common languages and can share personal information without permission. The researchers created a new way called SMP-BERT that helps computers better understand these reports by looking at the structure of the writing. This method worked much better than usual methods when detecting rare medical conditions in Hebrew, which is important for doctors to make accurate diagnoses. |
Keywords
» Artificial intelligence » Auc » Bert » Fine tuning » Natural language processing » Nlp » Prompt
