Create README.md

README.md

@@ -0,0 +1,54 @@
+---
+license: mit
+language:
+  - en
+tags:
+  - clinical-nlp
+  - cognitive-decline
+  - electronic-health-records
+  - transformer
+  - medical-ai
+  - healthcare
+---
+
+# CD-Tron: Cognitive Decline Detection from EHR using Large Clinical Language Model
+
+**Model Name:** CD-Tron
+
+**Author:** Hao Guan
+
+## Model Description
+
+CD-Tron is a fine-tuned large clinical language model based on [GatorTron](https://huggingface.co/UFNLP/gatortron-base) for the task of detecting cognitive decline from free-text clinical notes.
+
+The model was fine-tuned on real-world clinical data, and synthetic data can be used for demonstration.
+
+---
+
+## Intended Use
+
+- Task: Cognitive decline detection / screening
+- Input: Free-text clinical notes (EHR sections, progress notes, discharge summaries, etc.)
+- Output: Binary classification:  
+  - 0 = No cognitive decline  
+  - 1 = Cognitive decline detected
+
+This model is for research purposes and proof-of-concept demonstration.
+
+---
+
+## How to Use
+
+Example code to load and run inference:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("HAO-AI/cdtron-cognitive-decline")
+model = AutoModelForSequenceClassification.from_pretrained("HAO-AI/cdtron-cognitive-decline")
+
+text = "Patient presents with recent memory loss, confusion, and impaired attention..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(dim=1).item()
+print("Predicted label:", prediction)