---
language:
- en
license: mit
library_name: transformers
tags:
- medical
- cardiology
- emergency-medicine
- classification
- bert
datasets:
- MIMIC-III
metrics:
- accuracy
- f1
- auc
model-index:
- name: OHCA-Classifier-V9
  results:
  - task: 
      type: text-classification
      name: Out-of-Hospital Cardiac Arrest Detection
    metrics:
    - type: accuracy
      value: 0.86
    - type: f1
      value: 0.80
    - type: auc
      value: 0.905
---

# OHCA Classifier V9

## Model Description
This model classifies clinical notes to identify Out-of-Hospital Cardiac Arrest (OHCA) cases. It's based on BiomedNLP-PubMedBERT and trained on MIMIC-III data.

## Model Performance
- **AUC-ROC**: 0.905
- **Sensitivity**: 80%
- **Specificity**: 95% 
- **F1-Score**: 0.80
- **Optimal Threshold**: 0.120

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
tokenizer = AutoTokenizer.from_pretrained("monajm36/ohca-classifier-v9")
model = AutoModelForSequenceClassification.from_pretrained("monajm36/ohca-classifier-v9")

# Predict
def predict_ohca(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)
    return probs[0][1].item()  # OHCA probability

# Example
text = "Chief Complaint: Cardiac arrest. HPI: Patient found unresponsive..."
prob = predict_ohca(text)
print(f"OHCA Probability: {prob:.3f}")
```

## Training Data
- **Dataset**: MIMIC-III
- **Samples**: 330 clinical notes
- **Classes**: Binary (OHCA vs Non-OHCA)

## Disclaimer
This model is for research purposes only. Not intended for clinical decision-making.