ugaray96/biobert_ncbi_disease_ner
https://huggingface.co/ugaray96/biobert_ncbi_disease_ner# Model Description This model is a fine-tuned version of BioBERT on the NCBI disease dataset for named entity recognition (NER) of diseases. It can be used to extract disease mentions from unstructured text in the medical and biological domains.
Sourced from
- HuggingFace — ugaray96/biobert_ncbi_disease_ner
Related resources
datasets: - UMLS
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? . The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into…
View label scheme (20 labels for 1 components)
This model is a high-performance Named Entity Recognition (NER) model designed specifically for medical text. It identifies entities such as diseases, symptoms, procedures, medications, and healthcare providers with high precision and recall, making it ideal for clinical and healthcare applications.
genzeonplatform/healthcare-brain-laboratory-ner
by genzeonplatformHealthcare Brain Laboratory NER is a transformer-based clinical Named Entity Recognition model developed by Genzeon Platforms for automated extraction of laboratory test results, values, units, reference ranges, and abnormality flags from unstructured clinical text.
Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature