Summary of Clinical Information Extraction For Low-resource Languages with Few-shot Learning Using Pre-trained Language Models and Prompting, by Phillip Richter-pechanski et al.
Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting
by Phillip Richter-Pechanski, Philipp Wiesenbach, Dominic M. Schwab, Christina Kiriakou, Nicolas Geis, Christoph Dieterich, Anette Frank
First submitted to arxiv on: 20 Mar 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 research paper proposes innovative methods for automatically extracting medical information from clinical documents, addressing challenges such as high costs, limited interpretability, and restricted resources. The study builds upon recent advances in domain-adaptation and prompting techniques, which have shown promise in well-established settings. This work is the first to systematically evaluate these approaches in a low-resource setting, focusing on multi-class section classification on German doctor’s letters. The authors conduct extensive class-wise evaluations using Shapley values to validate the quality of their small training dataset and ensure interpretability. Notably, they demonstrate that a lightweight, domain-adapted pre-trained model prompted with just 20 shots outperforms traditional classification models by 30.5% in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to help doctors extract important medical information from old documents. It’s a big challenge because it takes a lot of expert knowledge and time, and the computer programs might not understand what they’re seeing. The researchers tried some new ways to make this process work better by teaching the computer to learn from just a few examples. They tested these methods on German doctor’s letters and found that one approach worked much better than others. This is important because it can help doctors and hospitals save time and money, and get the information they need more quickly. |
Keywords
* Artificial intelligence * Classification * Domain adaptation * Prompting
