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README.md

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+---
+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.