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En Biobert Ner Symptom
A named entity recognition model fine-tuned on BioBERT for detecting medical symptoms from clinical records.
Downloads 25
Release Time : 7/27/2023
Model Overview
This model is based on the BioBERT architecture, specifically designed to identify and extract medical symptom-related named entities from clinical texts.
Model Features
High-precision Symptom Recognition
Achieves an F1 score of 99.96% in named entity recognition tasks, accurately identifying symptom descriptions in clinical texts.
Based on BioBERT
Leverages the biomedical domain pre-training advantages of BioBERT, fine-tuned specifically for symptom recognition tasks.
Clinical Text Optimization
Specially optimized for clinical records and medical texts, capable of understanding professional medical terminology and expressions.
Model Capabilities
Clinical Text Analysis
Medical Symptom Recognition
Named Entity Extraction
Use Cases
Clinical Record Analysis
Electronic Medical Record Symptom Extraction
Automatically extracts patient-reported symptom information from electronic medical records
Accurately identifies common symptoms such as cough, sneezing, and rash
Clinical Research Data Mining
Extracts symptom-related information from clinical research literature for analysis
Helps researchers quickly identify relevant symptom reports
Patient Monitoring
Automatic Symptom Report Processing
Processes patient self-reported symptom descriptions
Identifies key symptoms such as dizziness, nausea, and difficulty breathing
Transformers
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