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