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monajm36/ohca-classifier-v8
ohca-classifier-v8 is a text classification model from monajm36. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
Author: Mona Moukaddem Model: monajm36/ohca-classifier-v8 Task: Binary text classification — Out-of-Hospital Cardiac Arrest (OHCA) vs Non-OHCA Base model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract
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Updated Nov 12, 2025
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From the Hugging Face model README
Author: Mona Moukaddem
Model: monajm36/ohca-classifier-v8
Task: Binary text classification — Out-of-Hospital Cardiac Arrest (OHCA) vs Non-OHCA
Base model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract
This model predicts whether a discharge note likely describes out-of-hospital cardiac arrest (OHCA).
It was fine-tuned from PubMedBERT on MIMIC-derived discharge notes using patient-level splits to prevent leakage.
⚠️ For research and decision support only. Not a substitute for clinical judgment.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "monajm36/ohca-classifier-v8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = """Chief Complaint: cardiac arrest
History of Present Illness: Patient found unresponsive at home... ROSC after EMS CPR..."""
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1).squeeze()
print(probs)
Threshold recommendations
Clinical Goal Threshold Behavior
High Sensitivity 0.28–0.32 Captures nearly all OHCA cases
Balanced 0.36 Validation-optimized
High Precision ≥0.50 Fewer false positives
At 0.36, validation yielded:
Sensitivity (Recall): 1.000
Specificity: 0.879
AUC: 0.971
Data & Training Summary
Source: MIMIC-derived discharge notes
Sections used: Chief Complaint, History of Present Illness
Splits: Train 210, Val 54, Test 66 (patient-level)
Max length: 512 tokens
Epochs: 5
Loss: Weighted cross-entropy
Sampler: Class-balanced
Hardware: CPU
Evaluation (Test Set)
Pred Non-OHCA Pred OHCA
Actual Non 51 7
Actual OHCA 0 9
Metrics:
Recall: 1.000
Specificity: 0.879
Precision: 0.562
NPV: 1.000
F1-score: 0.720
AUC: 0.971
Interpretation: The model captured all OHCA cases at the chosen threshold, with 7 false positives.
License
MIT
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M. Moukaddem. OHCA Classifier v8: PubMedBERT fine-tuned for Out-of-Hospital Cardiac Arrest