🔬 Biomedical Research Assistant

A comprehensive AI-powered system for biomedical literature analysis, semantic search, summarization, and trend discovery.

✨ Features

• 🔍 Semantic Search: AI-powered search through biomedical literature
• 📄 Auto-Summarization: Generate concise summaries of research papers
• 📈 Trend Analysis: Discover trending topics and research patterns
• 🎯 Topic Modeling: Identify and analyze research themes
• 🌐 Web Dashboard: Interactive Streamlit interface
• 🚀 REST API: FastAPI backend for integration
• 📊 Visualizations: Charts and graphs for trend analysis

🎯 Use Cases

• Researchers: Quickly find relevant papers and identify research gaps
• Clinicians: Stay updated with latest medical research
• Students: Understand research trends and topics
• Data Scientists: Analyze patterns in biomedical literature
• Institutions: Monitor research output and collaborations

🚀 Quick Start

1. Setup Environment

# Clone or download the project
cd biomedical-research-assistant

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

2. Configure Settings

# Copy configuration template
copy .env.template .env  # Windows
# cp .env.template .env  # Linux/Mac

# Edit .env with your email (required for PubMed API)
# [email protected]

3. Run Setup

# Check configuration
python main.py check

# Set up data pipeline (30-60 minutes first time)
python main.py setup

4. Start Using

# Start API server
python main.py server

# Start web dashboard (in another terminal)
python main.py dashboard

Open your browser to http://localhost:8501 for the dashboard!

📖 Detailed Documentation

See SETUP_INSTRUCTIONS.md for complete setup guide.

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Data Sources  │    │   AI Processing  │    │   User Interface│
├─────────────────┤    ├──────────────────┤    ├─────────────────┤
│ • PubMed API    │───▶│ • Text Cleaning  │───▶│ • Web Dashboard │
│ • Research Papers│    │ • Embeddings     │    │ • REST API      │
│ • Metadata      │    │ • Summarization  │    │ • Visualizations│
│ • MeSH Terms    │    │ • Topic Modeling │    │ • Search Results│
└─────────────────┘    └──────────────────┘    └─────────────────┘

🛠️ Components

Data Pipeline

• Data Ingestion: Fetches papers from PubMed using Entrez API
• Preprocessing: Cleans and structures text data
• Embeddings: Creates semantic vectors using Sentence Transformers
• Indexing: Builds FAISS index for fast similarity search

AI Models

• Embeddings: sentence-transformers/all-mpnet-base-v2
• Summarization: facebook/bart-large-cnn
• Topic Modeling: BERTopic with biomedical optimizations

Applications

• API Server: FastAPI with auto-generated documentation
• Web Dashboard: Streamlit with interactive visualizations
• CLI Tools: Command-line interface for all operations

📊 Example Queries

• "COVID-19 vaccine efficacy clinical trials"
• "cancer immunotherapy checkpoint inhibitors"
• "alzheimer disease biomarkers tau protein"
• "diabetes treatment metformin mechanism"
• "machine learning medical imaging"

🔧 Configuration Options

Research Domain

RESEARCH_DOMAIN=covid immunotherapy
MAX_PAPERS=5000
DATE_FROM=2020/01/01
DATE_TO=2024/12/31

Model Selection

# Standard (fast)
EMBEDDING_MODEL=sentence-transformers/all-mpnet-base-v2

# Biomedical (accurate)
EMBEDDING_MODEL=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract

Performance Tuning

# For testing
MAX_PAPERS=1000
TOP_K_RESULTS=10

# For production
MAX_PAPERS=10000
TOP_K_RESULTS=20

📈 Performance

Dataset Size	Setup Time	Search Speed	Memory Usage
1K papers	5-10 min	<100ms	2-4 GB
5K papers	20-30 min	<200ms	4-8 GB
10K+ papers	45-60 min	<300ms	8-16 GB

🤖 API Endpoints

Search

GET /search?q=covid+vaccine&top_k=10
POST /search

Summarization

GET /summarize?q=covid+vaccine&top_k=5
GET /paper/{pmid}/summary

Topics & Trends

GET /topics/trending?top_k=10
GET /topics/{topic_id}
GET /trends/general

Paper Details

GET /paper/{pmid}
GET /paper/{pmid}/similar

🎨 Dashboard Features

🔍 Search & Explore

• Semantic search with similarity scores
• Multi-paper summarization
• Similar paper recommendations
• Interactive result filtering

📈 Trending Topics

• Real-time trending topic identification
• Growth rate analysis
• Topic evolution over time
• Representative paper extraction

📊 Research Trends

• Publication trends by year/month
• Journal analysis and rankings
• Author collaboration patterns
• MeSH term frequency analysis

🎯 Paper Analysis

• Individual paper summaries
• Citation-style information
• Related paper discovery
• Metadata extraction

🔐 Privacy & Ethics

• No Personal Data: Only public research metadata is processed
• Medical Disclaimer: For research purposes only, not medical advice
• Rate Limiting: Respects PubMed API rate limits
• Open Source: Transparent algorithms and processing

🛡️ System Requirements

Minimum:

• Python 3.8+
• 8GB RAM
• 5GB storage
• Internet connection

Recommended:

• Python 3.9+
• 16GB+ RAM
• 20GB+ storage
• GPU (optional, for faster processing)

🚨 Important Notes

⚠️ Medical Disclaimer: This tool is for research and educational purposes only. It does not provide medical advice, diagnosis, or treatment recommendations. Always consult qualified healthcare professionals for medical decisions.

📚 Data Source: All data comes from publicly available research papers via PubMed/NCBI APIs.

🔄 Updates: The system processes research papers available up to your search date range. For the most current research, regularly update your dataset.

📞 Support & Contributing

• Issues: Report bugs and request features via GitHub issues
• Documentation: See setup guide and API documentation
• Community: Join discussions and share improvements
• Contributing: Pull requests welcome for new features and fixes

📜 License

This project is open source. See LICENSE file for details.

🙏 Acknowledgments

• NCBI/PubMed for providing access to biomedical literature
• Hugging Face for transformer models and libraries
• Streamlit & FastAPI for web framework components
• Scientific Community for open access research

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Built with ❤️ for the research community

Empowering discovery through AI-driven literature analysis