Update README.md

README.md

@@ -1,3 +1,91 @@
----
-license: mit
----
+---
+license: mit
+---
+---
+license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract
+tags:
+- clinical
+- healthcare
+- emergency-medicine
+- OHCA
+- PubMedBERT
+- MIMIC
+- patient-level-split
+model-index:
+- name: OHCA Classifier v8 (PubMedBERT fine-tuned)
+  results:
+  - task:
+      type: text-classification
+      name: Binary OHCA detection (OHCA vs non-OHCA)
+    dataset:
+      name: Internal (MIMIC-derived discharge notes)
+      type: text
+      split: test (patient-level)
+    metrics:
+    - type: recall
+      name: Sensitivity (Recall)
+      value: 1.000
+    - type: specificity
+      name: Specificity
+      value: 0.879
+    - type: precision
+      name: PPV (Precision)
+      value: 0.562
+    - type: npv
+      name: NPV
+      value: 1.000
+    - type: f1
+      name: F1-score
+      value: 0.720
+    - type: auc
+      name: ROC-AUC
+      value: 0.971
+---
+
+# OHCA Classifier v8 — PubMedBERT fine-tuned for cardiac arrest detection
+
+**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 is 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.
+
+---
+
+## What’s included
+- A multi-head fine-tuned classifier with a dedicated **binary OHCA head**
+- Trained tokenizer and configuration
+- Recommended **threshold guidance** for different clinical goals
+
+---
+
+## How to use
+
+### Quick start (Transformers)
+```python
+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)
+model.eval()
+
+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()
+p_ohca = float(probs[1])   # index 1 = OHCA, index 0 = Non-OHCA
+
+print({"p_ohca": p_ohca})