Noise reduction in BERT NER models for clinical entity extraction

Researchers developed a Noise Removal model to improve precision in clinical entity extraction using BERT-based Named Entity Recognition systems. The model uses advanced features like Probability Density Maps to identify weak vs strong predictions, reducing false positives by 50-90% in clinical NER applications.