medlitanno 1.1.1

Medical Literature Analysis and Annotation System with LLM-powered automation

MedLitAnno: Medical Literature Annotation System

MedLitAnno is a comprehensive medical literature analysis platform that combines automated annotation, PubMed search integration, and causal knowledge discovery. Extract structured information about bacteria-disease relationships from scientific texts, search and annotate PubMed literature automatically, and discover causal relationships through Mendelian Randomization (MR) analysis.

🌟 Features

🔍 PubMed Literature Search & Annotation

• Direct PubMed Integration: Search medical literature using keywords, diseases, bacteria, or recent publications
• Automated Annotation Pipeline: Seamlessly combine literature search with LLM-powered annotation
• Multiple Search Strategies: Basic search, disease-bacteria relationships, recent articles, keyword combinations
• Excel Export: Save search results with comprehensive metadata and citation information
• Rate-Limited API Access: Compliant with PubMed guidelines for responsible usage

📝 Advanced Medical Literature Annotation

• Multi-model Support: Use OpenAI, DeepSeek, DeepSeek Reasoner, or Qianwen models
• Automatic Position Matching: Intelligent text position calculation with 100% success rate
• Smart Content Recognition: LLM focuses on content identification while system handles positioning
• Robust Processing: Breakpoint resume and error retry mechanisms for network stability
• Comprehensive Annotation: Entity recognition, relation extraction, evidence detection
• Batch Processing: Process entire directories of Excel files with progress monitoring
• Format Conversion: Export to Label Studio compatible format

MRAgent: Causal Knowledge Discovery

• Automated Literature Analysis: Scans scientific literature to discover potential exposure-outcome pairs
• Causal Inference: Performs Mendelian Randomization using GWAS data
• Knowledge Discovery Mode: Autonomously identifies potential causal factors for diseases
• Causal Validation Mode: Validates specific causal hypotheses
• GWAS Integration: Seamless integration with OpenGWAS database

🚀 Installation

From PyPI (Recommended)

pip install medlitanno

From Source

# Clone the repository
git clone https://github.com/chenxingqiang/medlitanno.git
cd medlitanno

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install -e .

⚙️ Configuration

API Keys and Environment Setup

Set your API keys and configuration as environment variables:

# For LLM models
export DEEPSEEK_API_KEY="your-deepseek-api-key"
export QIANWEN_API_KEY="your-qianwen-api-key"
export OPENAI_API_KEY="your-openai-api-key"  # Optional

# For PubMed search (required for literature search)
export PUBMED_EMAIL="[e-mail]"  # Required by PubMed API
export PUBMED_TOOL="medlitanno"              # Tool identifier

# For MR analysis (optional)
export OPENGWAS_JWT="your-opengwas-jwt-token"

Configuration File

You can also create a .env file in your project directory:

# Copy the example configuration
cp config/env.example .env
# Edit .env with your actual API keys and settings

📊 Usage

🔍 PubMed Literature Search

Command Line Interface

# Search PubMed and automatically annotate results
medlitanno search "Helicobacter pylori gastric cancer" --max-results 50

# Search for disease-bacteria relationships
medlitanno search "diabetes microbiome" --disease "diabetes" --bacteria "gut bacteria"

# Search recent publications (last 30 days)
medlitanno search "COVID-19 microbiome" --recent-days 30

# Search and save results to Excel (without annotation)
medlitanno search "inflammatory bowel disease" --output-dir ./results --max-results 100

Python API

from medlitanno.pubmed import PubMedSearcher, search_and_annotate
import os

# Initialize PubMed searcher
searcher = PubMedSearcher(
    email=os.environ.get("PUBMED_EMAIL"),
    tool="medlitanno"
)

# Search for articles
results = searcher.search("Helicobacter pylori gastric cancer", max_results=50)
print(f"Found {len(results.articles)} articles")

# Search and automatically annotate
search_and_annotate(
    query="microbiome inflammatory disease",
    api_key=os.environ.get("DEEPSEEK_API_KEY"),
    model="deepseek-chat",
    max_results=20,
    output_dir="./results"
)

📝 Medical Literature Annotation

Command Line Interface

# Annotate medical literature
medlitanno annotate --data-dir datatrain --model deepseek-chat

# Use DeepSeek Reasoner for enhanced inference
medlitanno annotate --data-dir datatrain --model deepseek-reasoner --model-type deepseek

Python API

from medlitanno.annotation import MedicalAnnotationLLM
import os

# Initialize the annotator with automatic position matching
annotator = MedicalAnnotationLLM(
    api_key=os.environ.get("DEEPSEEK_API_KEY"),
    model="deepseek-chat",
    model_type="deepseek"
)

# Annotate text with automatic position calculation
text = "Helicobacter pylori infection is associated with gastric cancer."
result = annotator.annotate_text(text)

# Print results with position information
print(f"Entities: {result.entities}")
for entity in result.entities:
    print(f"  - {entity.text} ({entity.label}): pos {entity.start_pos}-{entity.end_pos}, confidence: {entity.confidence:.2f}")

print(f"Relations: {result.relations}")
print(f"Evidences: {result.evidences}")
for evidence in result.evidences:
    print(f"  - {evidence.text}: pos {evidence.start_pos}-{evidence.end_pos}, confidence: {evidence.confidence:.2f}")

MRAgent: Causal Knowledge Discovery

Command Line Interface

# Knowledge Discovery mode
medlitanno mr --outcome "back pain" --model gpt-4o

# Causal Validation mode
medlitanno mr --exposure "osteoarthritis" --outcome "back pain" --mode causal

Python API

from medlitanno.mragent import MRAgent, MRAgentOE
import os

# Knowledge Discovery mode
agent = MRAgent(
    outcome="back pain",
    AI_key=os.environ.get("OPENAI_API_KEY"),
    LLM_model="gpt-4o",
    gwas_token=os.environ.get("OPENGWAS_JWT")
)
agent.run()

# Causal Validation mode
agent_oe = MRAgentOE(
    exposure="osteoarthritis",
    outcome="back pain",
    AI_key=os.environ.get("OPENAI_API_KEY"),
    LLM_model="gpt-4o",
    gwas_token=os.environ.get("OPENGWAS_JWT")
)
agent_oe.run()

📄 Output Format

PubMed Search Results

PubMed search provides:

1. Article Metadata: Title, abstract, authors, publication date, journal
2. Citation Information: PMID, DOI, publication details
3. Search Statistics: Total results, query details, search timestamp
4. Excel Export: Structured data export for further analysis

Annotation System

The annotation system extracts structured information with automatic position matching:

1. Entities: Bacteria and Disease mentions with precise text positions
2. Relations: Connections between entities with relation types
3. Evidences: Text spans supporting the relations with confidence scores
4. Position Statistics: Success rates and confidence metrics for quality assessment

Relation Types

• contributes_to: Bacteria contributes to disease development
• ameliorates: Bacteria improves or alleviates disease
• correlated_with: Bacteria and disease show correlation
• biomarker_for: Bacteria serves as a biomarker for disease

Position Matching Features

• 100% Success Rate: Intelligent matching strategies ensure reliable position detection
• Multiple Strategies: Exact, case-insensitive, normalized, fuzzy, and partial matching
• Confidence Scoring: Average confidence >0.8 for quality assessment
• Automatic Fallback: Progressive matching strategies for robust results

MRAgent System

MRAgent provides:

1. Literature Analysis: Summary of relevant scientific papers
2. Potential Exposures: List of potential causal factors
3. MR Results: Statistical evidence for causal relationships
4. Visualizations: Forest plots and other visual representations
5. Recommendations: Insights for further research

🚀 Performance

Literature Search

• PubMed Integration: Real-time search with rate limiting (3 requests/second)
• Search Speed: ~2-5 seconds per query (depends on result count)
• Result Processing: Handles thousands of articles efficiently

Annotation System

• Processing Speed: ~30-60 seconds per document (depends on model and text length)
• Position Matching: 100% success rate with <1 second processing per document
• Batch Processing: Optimized for large-scale literature analysis
• Accuracy: Comparable to manual annotation in controlled tests

MR Analysis

• Literature Processing: Handles hundreds of articles and GWAS datasets efficiently
• Causal Discovery: Automated analysis of complex exposure-outcome relationships

💪 Stability & Reliability

Network Resilience

• Automatic Retry: Smart retry mechanisms for network instability
• Rate Limiting: Compliant with API guidelines and rate limits
• Connection Recovery: Robust handling of network interruptions

Processing Reliability

• Breakpoint Resume: Automatically continues from the last processed file
• Error Recovery: Graceful handling of parsing and processing errors
• Progress Monitoring: Real-time tracking with detailed statistics
• Data Validation: Comprehensive validation of results and outputs

📋 Project Structure

medlitanno/
├── src/                    # Source code
│   └── medlitanno/         # Main package
│       ├── annotation/     # Annotation system with position matching
│       ├── pubmed/         # PubMed search integration
│       ├── common/         # Shared utilities and base classes
│       └── mragent/        # MR analysis (optional, requires biopython)
├── docs/                   # Documentation
│   ├── PUBMED_SEARCH_GUIDE.md  # PubMed search usage guide
│   ├── README_annotation.md    # Annotation system documentation
│   └── ...
├── examples/               # Example scripts and demos
│   ├── pubmed_search_demo.py   # PubMed search examples
│   ├── position_matching_demo.py # Position matching examples
│   └── ...
├── tests/                  # Unit tests
├── scripts/                # Utility scripts
├── config/                 # Configuration files
│   ├── env.example         # Environment configuration template
│   └── requirements.txt    # Dependencies
├── CHANGELOG.md            # Version history
└── ...

📚 Documentation

• PubMed Search Guide: Complete guide for literature search functionality
• Annotation Documentation: Detailed annotation system documentation
• Setup Guide: Installation and configuration instructions
• Examples: Working examples and demo scripts

🔄 Version History

See CHANGELOG.md for detailed version history and feature updates.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

# Clone the repository
git clone https://github.com/chenxingqiang/medlitanno.git
cd medlitanno

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest tests/

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

For questions or feedback, please contact [e-mail].

🙏 Acknowledgments

• MRAgent: Innovative automated agent for causal knowledge discovery via Mendelian Randomization
• PyMed: Python library for PubMed API access
• OpenGWAS: GWAS summary data for causal inference

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Latest Version: v1.1.0 - Now with PubMed search integration and automatic position matching!