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En Core Med7 Lg
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En Core Med7 Lg
Developed by kormilitzin
Med7 is a model focused on clinical natural language processing for electronic health records, used to identify medication-related information in medical texts.
Downloads 828
Release Time : 3/2/2022
Model Overview
This model is primarily used to recognize and classify medication-related named entities from clinical texts, such as dosage, drug names, duration, etc.
Model Features
Specialized for Medical Domain
Optimized specifically for electronic health records and clinical texts
High-Precision Recognition
Achieves an F1-score of 87.7% in medication-related entity recognition tasks
Seven Entity Types Recognition
Capable of recognizing seven types of entities: dosage, drug, duration, form, frequency, route, and strength
Model Capabilities
Medical Text Analysis
Medication Information Extraction
Clinical Entity Recognition
Use Cases
Medical Information Processing
Electronic Health Record Analysis
Extract medication usage information from patient electronic medical records
Automatically identify drug names, dosages, and administration frequencies
Clinical Research Support
Analyze large volumes of clinical text data to support drug research
Quickly extract medication-related information to improve research efficiency
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