metadata
language: en
tags:
- named-entity-recognition
- medical
- radiology
- spacy
license: apache-2.0
Radiology Report NER Model
Named Entity Recognition model for extracting structured information from chest X-ray radiology reports.
Model Details
- Architecture: spaCy 3.8 with HashEmbedCNN
- Training Data: Indiana University Chest X-Ray Reports (2,674 reports)
- Entity Types: ANATOMY, OBSERVATION
- F-Score: 99.94%
- Precision: 99.93%
- Recall: 99.95%
Usage
import spacy
# Load the model
nlp = spacy.load("MakPr016/radiology-ner-model")
# Process text
text = "The cardiac silhouette is within normal limits. No pleural effusion."
doc = nlp(text)
# Extract entities
for ent in doc.ents:
print(f"{ent.text} - {ent.label_}")
Entity Labels
- ANATOMY: Body parts and anatomical structures (e.g., lung, cardiac silhouette, diaphragm)
- OBSERVATION: Medical findings and observations (e.g., consolidation, effusion, normal)
Training Details
- Training Steps: 11,500
- Best Model: Epoch 62
- Dataset: Indiana University Chest X-Ray Reports
- Validation: 573 test reports
Limitations
- Optimized for chest X-ray reports
- May not perform well on other body parts (knee, spine, etc.)
- Trained on English language reports only