README.md

---
license: mit
---
### 🩺 Clinical Diagnosis Application & medReport Model

Welcome to the Clinical Diagnosis Application, a NLP-powered deep learning solution for automated medical diagnosis based on clinical notes. This project leverages BioBERT, Natural Language Processing, and Hugging Face Transformers to analyze patient reports and predict diseases with high accuracy.

🚀 Live Model Hosted on Hugging Face: DrSyedFaizan/medReport

🔬 Overview
medReport is a fine-tuned BioBERT model trained on clinical text data to predict diseases based on patient reports. The associated Clinical Diagnosis App allows users to upload medical notes (PDF/TXT) and receive disease predictions along with recommended medications and specialists.

✨ Features
✅ Fine-tuned BioBERT Model for medical text classification
✅ Predict diseases from clinical notes
✅ Extract text from PDFs and TXT files
✅ Recommend medications & specialists based on prediction
✅ Streamlit-powered web app for easy access
✅ Deployable on Hugging Face Spaces / Local Server

📂 Project Structure

📁 Clinical-Diagnosis-App/
│── 📂 patient_model/         # Trained BioBERT model files
│── 📂 results/               # Model training results & logs
│── 📂 sample_data/           # Sample clinical reports
│── 📜 app.py                 # Streamlit-based UI for predictions
│── 📜 requirements.txt       # Required dependencies
│── 📜 README.md              # Documentation
│── 📜 label_encoder.pkl      # Pre-trained Label Encoder
│── 📜 clinical_notes.csv     # Sample dataset
🚀 Installation & Setup
1️⃣ Clone the Repository

git clone https://github.com/SYEDFAIZAN1987/Clinical-Diagnosis-Application-using-Natural-Language-Processing.git
cd Clinical-Diagnosis-Application-using-Natural-Language-Processing
2️⃣ Install Dependencies

pip install -r requirements.txt
3️⃣ Run the Applicationbash

streamlit run app.py
The app will launch at http://localhost:8501 🎉

📌 Model Details
The medReport model is fine-tuned on a clinical notes dataset using BioBERT, a biomedical NLP model. It has been trained for multi-label classification, allowing it to predict diseases from unstructured clinical text.

🔗 Load the Model
You can access the trained model directly via Hugging Face:

python
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from transformers import BertForSequenceClassification, BertTokenizer
from huggingface_hub import hf_hub_download
import pickle
import torch

# Load Model & Tokenizer
model = BertForSequenceClassification.from_pretrained("DrSyedFaizan/medReport")
tokenizer = BertTokenizer.from_pretrained("DrSyedFaizan/medReport")

# Load Label Encoder
label_encoder_path = hf_hub_download(repo_id="DrSyedFaizan/medReport", filename="label_encoder.pkl")
with open(label_encoder_path, "rb") as f:
    label_encoder = pickle.load(f)
📊 Performance Metrics
Metric	Score
Accuracy	100$

✅ Trained on BioBERT
✅ Optimized with AdamW
✅ Fine-tuned for Clinical NLP

📖 Usage
🔹 Predict Disease from a Clinical Note
python
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def predict_disease(text, model, tokenizer, label_encoder):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    predicted_label = torch.argmax(logits, dim=1).item()
    return label_encoder.inverse_transform([predicted_label])[0]
🎨 Web App UI (Streamlit)
The Streamlit UI allows drag & drop of PDF/TXT files for quick disease predictions.

📥 Upload Clinical Notes
1️⃣ Upload clinical notes (PDF or TXT)
2️⃣ Extract text from reports
3️⃣ Predict disease
4️⃣ Get medication & specialist recommendations

🏥 Example Predictions
Clinical Note	Predicted Disease	Medications	Specialists
"Patient reports persistent heartburn..."	Gastroesophageal Reflux Disease (GERD)	Omeprazole, Ranitidine	Gastroenterologist
"Male patient with history of smoking, chronic cough..."	Chronic Obstructive Pulmonary Disease (COPD)	Tiotropium, Albuterol	Pulmonologist
"Elderly patient with diabetes, experiencing numbness..."	Diabetic Neuropathy	Metformin, Insulin	Endocrinologist
🌍 Deployment Options
1️⃣ Run Locally with Streamlit

bash
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streamlit run app.py
2️⃣ Deploy on Hugging Face Spaces

Create a Streamlit space on Hugging Face
Upload the repository
Add a requirements.txt file
Run app.py automatically
3️⃣ Deploy on Cloud (AWS, GCP, Azure)

Use FastAPI + Uvicorn
Deploy via Docker / Kubernetes
🛠️ Tech Stack
✔ BioBERT (Fine-Tuned)
✔ Transformers (Hugging Face)
✔ PyTorch (Deep Learning)
✔ Streamlit (UI Framework)
✔ Hugging Face Hub (Model Hosting)

🧑‍💻 Contribution
🤝 Contributions are welcome!
If you'd like to improve the model or app, feel free to fork the repo and submit a pull request.

Fork the repository
Clone locally
Create a branch (git checkout -b feature-new)
Commit changes (git commit -m "Added feature X")
Push & Submit a PR
📩 Contact
💡 Author: Syed Faizan, MD
📧 Email: faizan.s@northeastern.edu
🤖 Hugging Face: DrSyedFaizan
📂 GitHub: SYEDFAIZAN1987