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Bert Large Uncased Med Ner
This model is used to identify drug-related entities in medical texts, including drug names, dosage, duration, frequency, and reason for medication.
Downloads 86
Release Time : 3/2/2022
Model Overview
A named entity recognition model trained on the i2b2 (now n2c2) 2009 drug task dataset, specifically designed to extract drug-related information from medical texts.
Model Features
Multi-category Entity Recognition
Capable of simultaneously identifying five types of entities: drug names, dosage, duration, frequency, and reason for medication.
Specialized for Medical Domain
Optimized specifically for medical texts, capable of understanding medical terminology and drug-related expressions.
Based on Standard Dataset
Trained on the i2b2/n2c2 2009 standard dataset, ensuring reliable model performance.
Model Capabilities
Medical Text Analysis
Drug Information Extraction
Named Entity Recognition
Clinical Record Processing
Use Cases
Medical Information Processing
Electronic Medical Record Analysis
Automatically extract patient medication information from electronic medical records
Improves efficiency in medical record processing and reduces manual extraction time
Clinical Research Data Preparation
Automatically collect and analyze patient medication data for drug research
Accelerates the research data collection process
Healthcare Applications
Medication Management System
Automatically identify and classify patient medication information
Improves medication management processes
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