monajm36/ohca-classifier-v11

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)

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:

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.

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