Summary of Named Clinical Entity Recognition Benchmark, by Wadood M Abdul et al.
Named Clinical Entity Recognition Benchmark
by Wadood M Abdul, Marco AF Pimentel, Muhammad Umar Salman, Tathagata Raha, Clément Christophe, Praveen K Kanithi, Nasir Hayat, Ronnie Rajan, Shadab Khan
First submitted to arxiv on: 7 Oct 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 proposed benchmark aims to evaluate language models in healthcare by recognizing named clinical entities in clinical narratives. This NLP task is essential for supporting applications such as automated coding, clinical trial cohort identification, and clinical decision support. The authors introduce a benchmark for evaluating language models’ ability to extract structured information from unstructured text, promoting the development of more accurate models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new benchmark helps evaluate language models in healthcare by recognizing important medical terms in patient notes. This makes it easier to develop better computer systems that can help doctors with tasks like identifying patients for clinical trials and providing personalized treatment plans. |
Keywords
» Artificial intelligence » Nlp
