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MakPr016/
radiology-ner-model

Name: radiology-ner-model
Availability: InStock

spacy

named-entity-recognition

medical

radiology

apache-2.0

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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

python

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