Summary of Medical Concept Normalization in a Low-resource Setting, by Tim Patzelt
Medical Concept Normalization in a Low-Resource Setting
by Tim Patzelt
First submitted to arxiv on: 6 Sep 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 The proposed research explores medical concept normalization in low-resource settings, focusing on German lay texts. Current methods are investigated for their shortcomings when applied to this task. To address the lack of suitable datasets, an annotated dataset is created from a German medical online forum using concepts from the Unified Medical Language System. The study finds that multilingual Transformer-based models outperform string similarity methods, while incorporating contextual information leads to inferior results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers are trying to improve how computers understand medical texts in languages other than English. They’re focusing on German because there isn’t much data available for this language. To solve the problem, they created their own dataset using online forum posts and tested different computer models. The results show that some models work better than others, but they still need to fix many mistakes. |
Keywords
» Artificial intelligence » Transformer
