OpenMed NER PathologyDetect SuperClinical 434M EU AI Act Compliance Profile

🧬 OpenMed-NER-PathologyDetect-SuperClinical-434M

Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset

📋 Model Overview

This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the ncbi dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.

🎯 Key Features

• High Precision: Optimized for biomedical entity recognition
• Domain-Specific: Trained on curated NCBI_DISEASE dataset
• Production-Ready: Validated on clinical benchmarks
• Easy Integration: Compatible with Hugging Face Transformers ecosystem

🏷️ Supported Entity Types

This model can identify and classify the following biomedical entities:

• B-Disease
• I-Disease

📊 Dataset

NCBI Disease corpus is a comprehensive resource for disease name recognition and concept normalization.

The NCBI Disease corpus is a gold-standard dataset containing 793 PubMed abstracts with 6,892 disease mentions mapped to 790 unique disease concepts from Medical Subject Headings (MeSH) and Online Mendelian Inheritance in Man (O

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