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--- |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract |
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tags: |
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- clinical |
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- healthcare |
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- emergency-medicine |
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- OHCA |
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- PubMedBERT |
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- MIMIC |
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- patient-level-split |
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model-index: |
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- name: OHCA Classifier v8 (PubMedBERT fine-tuned) |
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results: |
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- task: |
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type: text-classification |
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name: Binary OHCA detection (OHCA vs non-OHCA) |
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dataset: |
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name: Internal (MIMIC-derived discharge notes) |
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type: text |
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split: test (patient-level) |
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metrics: |
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- type: recall |
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name: Sensitivity (Recall) |
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value: 1.000 |
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- type: specificity |
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name: Specificity |
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value: 0.879 |
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- type: precision |
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name: PPV (Precision) |
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value: 0.562 |
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- type: npv |
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name: NPV |
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value: 1.000 |
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- type: f1 |
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name: F1-score |
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value: 0.720 |
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- type: auc |
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name: ROC-AUC |
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value: 0.971 |
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--- |
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# OHCA Classifier v8 — PubMedBERT fine-tuned for cardiac arrest detection |
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**Author:** Mona Moukaddem |
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**Model:** `monajm36/ohca-classifier-v8` |
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**Task:** Binary text classification — *Out-of-Hospital Cardiac Arrest (OHCA) vs Non-OHCA* |
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**Base model:** `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract` |
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This model predicts whether a discharge note likely describes **out-of-hospital cardiac arrest (OHCA)**. |
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It was fine-tuned from PubMedBERT on MIMIC-derived discharge notes using **patient-level splits** to prevent leakage. |
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> ⚠️ For research and decision support only. Not a substitute for clinical judgment. |
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--- |
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## How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_id = "monajm36/ohca-classifier-v8" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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text = """Chief Complaint: cardiac arrest |
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History of Present Illness: Patient found unresponsive at home... ROSC after EMS CPR...""" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.softmax(logits, dim=-1).squeeze() |
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print(probs) |
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Threshold recommendations |
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Clinical Goal Threshold Behavior |
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High Sensitivity 0.28–0.32 Captures nearly all OHCA cases |
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Balanced 0.36 Validation-optimized |
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High Precision ≥0.50 Fewer false positives |
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At 0.36, validation yielded: |
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Sensitivity (Recall): 1.000 |
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Specificity: 0.879 |
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AUC: 0.971 |
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Data & Training Summary |
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Source: MIMIC-derived discharge notes |
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Sections used: Chief Complaint, History of Present Illness |
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Splits: Train 210, Val 54, Test 66 (patient-level) |
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Max length: 512 tokens |
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Epochs: 5 |
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Loss: Weighted cross-entropy |
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Sampler: Class-balanced |
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Hardware: CPU |
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Evaluation (Test Set) |
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Pred Non-OHCA Pred OHCA |
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Actual Non 51 7 |
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Actual OHCA 0 9 |
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Metrics: |
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Recall: 1.000 |
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Specificity: 0.879 |
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Precision: 0.562 |
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NPV: 1.000 |
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F1-score: 0.720 |
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AUC: 0.971 |
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Interpretation: The model captured all OHCA cases at the chosen threshold, with 7 false positives. |
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License |
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MIT |
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Citation |
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sql |
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Copy code |
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M. Moukaddem. OHCA Classifier v8: PubMedBERT fine-tuned for Out-of-Hospital Cardiac Arrest |
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