🧠 RAGnosis – Clinical Reasoning Assistant

An AI-powered assistant for diagnostic reasoning using Retrieval-Augmented Generation (RAG) on annotated patient notes from the MIMIC-IV-Ext DiReCT dataset.

Built with ❤️ using Streamlit, SentenceTransformers, FAISS, and Transformers.

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🩺 Overview

RAGnosis combines dense retrieval and large language models to answer clinical questions using real-world diagnostic notes. It retrieves relevant clinical cases and generates AI-assisted diagnostic suggestions using context.

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🚀 Features

• 🔍 Dense Retrieval of similar patient cases (FAISS + MiniLM)
• 💬 Context-aware Diagnosis Generation (LLMs)
• 📊 ROUGE, BLEU, and BLEU-1 evaluation support
• 🧠 Real-time clinical query interface via Streamlit
• 🧪 Optimized for GPU acceleration using CUDA
• 💅 Beautiful UI with custom styling

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📸 Demo

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📸 Output

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🗃️ Dataset Structure

data/
├── diagnosis_flowchart/      # Diagnostic KG for each disease
├── finished_cases/           # Annotated notes (JSON)
├── clinical_embeddings.pkl   # Precomputed note embeddings

🛠️ Installation

git clone https://github.com/AbsarRaashid3/RAGnosis.git
cd RAGnosis

# Optional: create a virtual environment
python -m venv .venv
.venv/Scripts/activate

# Install dependencies
pip install -r requirements.txt

⚙️ Usage 🔹 1. Preprocess Clinical Notes

python src/preprocessing.py

🔹 2. Run the Streamlit Frontend

streamlit run app.py

Then go to http://localhost:8501

🧪 Evaluation

You can test generation quality using:

python src/evaluation_utils.py

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📸 evaluation_utils

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Includes:

• ROUGE
• BLEU (standard)
• BLEU-1 (unigram precision)

📁 Project Structure

RAGnosis/
├── app.py                   # Streamlit frontend
├── src/
│   ├── preprocessing.py     # Embedding + data prep
│   ├── retriever.py         # FAISS-based retriever
│   ├── generator.py         # LLM-based generator
│   └── evaluation_utils.py  # Evaluation metrics
├── assets/                  # Banner / UI assets
└── .streamlit/config.toml   # Custom Streamlit theme

💻 Technologies Used

• SentenceTransformers
• Transformers (HuggingFace)
• FAISS
• Streamlit
• Evaluate (HuggingFace)

📌 Example Clinical Queries

"Does the patient have COPD?"
"Can this be diagnosed as a duodenal ulcer?"
"Is shortness of breath explained by heart failure?"
"Should this patient undergo endoscopy?"

Created by Muhammad Absar Raashid