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
# 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
@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