README.md

---
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

```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