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README.md

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+---
+language: en
+license: mit
+tags:
+- medical
+- clinical-notes
+- cardiac-arrest
+- ohca
+- biomedical-nlp
+- transformers
+- pubmedbert
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+# OHCA Classifier V11: Temporal + Location-Aware Model
+
+## Model Description
+
+A transformer-based deep learning model for automatically identifying Out-of-Hospital Cardiac Arrest (OHCA) cases from clinical notes.
+
+**Key Innovation:** Combines semantic understanding (PubMedBERT) with explicit location and temporal features to distinguish OHCA from in-hospital cardiac arrest (IHCA).
+
+## Training Data
+
+- **Dataset**: MIMIC-III clinical notes
+- **Size**: 330 notes (47 OHCA, 283 Non-OHCA)
+- **Split**: 70% train / 15% validation / 15% test
+- **Average note length**: 13,042 characters
+
+## Performance (C19 Validation - 647 notes)
+
+| Metric | Score |
+|--------|-------|
+| **Sensitivity** | 92.1% |
+| **Specificity** | 89.4% |
+| **Precision** | 79.9% |
+| **F1-Score** | 0.856 |
+| **AUC-ROC** | 0.956 |
+
+## Model Architecture
+
+**Base Model**: `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract`
+
+**Input Features (775 dimensions):**
+- BERT embeddings: 768
+- Location features: 2
+  - OHCA location indicator count (22 phrases)
+  - IHCA location indicator count (25 phrases)
+- Temporal features: 5
+  - Arrest timing score (when arrest occurred)
+  - First location outside hospital (binary)
+  - First location inside hospital (binary)
+  - Movement outside→inside count
+  - Movement inside→inside count
+
+**Classifier**: 3-layer MLP (775 → 512 → 256 → 2)
+
+## Key Features
+
+### Location Features
+**OHCA indicators**: home, EMS, scene, field, bystander, ambulance, paramedics, etc.
+
+**IHCA indicators**: floor, ICU, ward, room, bed, code blue, admitted, telemetry, etc.
+
+### Temporal Features
+Captures the **story** of what happened:
+- **When**: Before arrival vs during hospitalization
+- **Where it started**: First location mentioned (inside/outside)
+- **How patient moved**: Direction of transitions (outside→inside vs inside→inside)
+
+## Usage
+```python
+# Note: Requires custom model class and feature extraction
+# See model files for implementation details
+
+from transformers import AutoTokenizer
+import torch
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("monajm36/ohca-classifier-v11")
+
+# Example clinical note
+note = """
+Patient found unresponsive at home by family. 911 called.
+EMS arrived, initiated CPR. ROSC achieved in field.
+Transported to ED.
+"""
+
+# Extract features (requires custom code)
+# location_features = extract_location_features(note)
+# temporal_features = extract_temporal_features(note)
+
+# Tokenize
+inputs = tokenizer(note, return_tensors="pt", max_length=512, truncation=True)
+
+# Predict (requires loading custom model architecture)
+# ...
+```
+
+## Threshold Selection
+
+Choose threshold based on your clinical use case:
+
+| Use Case | Threshold | Sensitivity | Specificity | F1 |
+|----------|-----------|-------------|-------------|-----|
+| **Screening (High Recall)** | 0.14 | 92.1% | 89.4% | 0.856 |
+| **Balanced** | 0.74 | 82.3% | 93.2% | 0.831 |
+| **Research (High Precision)** | 0.85 | 75.4% | 95.0% | 0.810 |
+
+## Limitations
+
+- Trained on single institution (MIMIC-III)
+- May not generalize to all clinical documentation styles
+- IHCA false positive rate: ~28.5% at optimal threshold
+- Requires feature extraction code (not included in model weights)
+- Best performance on notes with clear EMS or location context
+
+## Model Versions
+
+This is **Version 11** - the latest and most accurate version.
+
+| Version | Key Features | F1-Score |
+|---------|--------------|----------|
+| V9 | BERT only | 0.732 |
+| V10 | + Location features | 0.814 |
+| **V11** | **+ Temporal features** | **0.856** |
+
+## Citation
+```bibtex
+@misc{moukaddem2025ohca,
+  author = {Moukaddem, Mona},
+  title = {OHCA Classifier V11: Temporal and Location-Aware Model for Out-of-Hospital Cardiac Arrest Identification},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/monajm36/ohca-classifier-v11}}
+}
+```
+
+## Contact
+
+For questions, issues, or collaboration opportunities, please open an issue on the model repository.
+
+## Model Card Authors
+
+Mona Moukaddem
+
+## Acknowledgments
+
+- Training data: MIMIC-III Clinical Database
+- Validation data: UChicago C19 dataset
+- Base model: Microsoft BiomedNLP-PubMedBERT