Authors: Wadood M Abdul, Marco AF Pimentel, Muhammad Umar Salman, Tathagata Raha, Clément Christophe, Praveen K Kanithi, Nasir Hayat, Ronnie Rajan, Shadab Khan

Published on: October 07, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2410.05046

Summary

• What is new: Introducing a leader board to evaluate language models in clinical entity recognition using standardized clinical datasets.
• Why this is important: Extracting structured information from clinical narratives accurately and efficiently.
• What the research proposes: Creating a Named Clinical Entity Recognition Benchmark and standardizing entities across datasets.
• Results: Provided a comprehensive evaluation of models’ performances, including trends and limitations.

Technical Details

Technological frameworks used: NLP Benchmarking Framework

Models used: Encoder and Decoder Architectures

Data used: Curated clinical datasets standardized by OMOP Common Data Model

Potential Impact

Healthcare providers, medical coding companies, clinical trial coordinators, and digital health startups.

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