Extension usage examples:
Here's how our browser extension sees the article:
Article summary:
1. This article investigates the effectiveness of transformer-based (BERT) models for normalizing biomedical and clinical entities in large-scale electronic health records.
2. The model, EhrBERT, was trained using 1.5 million electronic health record records and compared with two state-of-the-art normalization systems, MetaMap and Disease Name Normalization (DNorm).
3. Results showed that EhrBERT outperformed both MetaMap and DNorm on all three annotated corpora for biomedical and clinical entity normalization.
Article analysis:
This article is a reliable source of information as it provides an empirical study on the effectiveness of transformer-based (BERT) models for normalizing biomedical and clinical entities in large-scale electronic health records. The authors have provided evidence to support their claims by comparing the performance of their model, EhrBERT, with two state-of-the-art normalization systems, MetaMap and Disease Name Normalization (DNorm). Furthermore, they have also discussed the potential implications of their findings for future research in this field.
The article does not appear to be biased or one sided as it presents both sides equally by providing evidence to support its claims. Additionally, there are no unsupported claims or missing points of consideration as the authors have provided a detailed explanation of their methodology and results. Furthermore, there is no promotional content or partiality as the authors have presented an unbiased view on their findings. Lastly, possible risks are noted as the authors discuss potential implications for future research in this field.