MIMIC-IV Oncology Pathway Mapping Engine

A clinical pathway analysis platform that analyzes real oncology patient data from the MIMIC-IV dataset using Google BigQuery.

🎯 Key Features

• Real Patient Data Analysis: Processes actual oncology cohorts from MIMIC-IV
• Clinical Pathway Visualization: Interactive Sankey diagrams and timeline views
• Digital Twin Matching: Find similar patients based on demographics and diagnosis
• Survival Analysis: Kaplan-Meier curves with stratification options
• BigQuery Integration: Scalable processing of large medical datasets
• HIPAA Compliant: All data remains in secure BigQuery environment

🚀 Quick Start

Prerequisites

• Python 3.9+
• Google Cloud account with BigQuery access
• MIMIC-IV dataset access via PhysioNet

Installation

# Clone the repository
git clone https://github.com/mirzafarangi/mimic-oncology-bigquery-git.git
cd mimic-oncology-bigquery-git

# Install dependencies
pip install -r requirements.txt

# Configure Google Cloud
gcloud auth application-default login
gcloud config set project your-project-id

Running the Application

streamlit run app.py

Navigate to http://localhost:8501 in your browser.

📊 Cancer Types Analyzed

Hematologic Malignancies

• Hodgkin & Non-Hodgkin Lymphoma
• Multiple Myeloma
• Acute & Chronic Leukemias

Solid Tumors

• Thyroid: Papillary, Follicular, Medullary, Anaplastic
• Gastrointestinal: Colorectal, Gastric, Pancreatic, Hepatocellular
• Thoracic: Lung (NSCLC, SCLC)
• Genitourinary: Prostate, Renal Cell, Bladder
• Gynecologic: Ovarian, Endometrial, Cervical
• Others: Breast, Brain, Melanoma

🔧 Configuration

Create a .env file in the project root:

PROJECT_ID=your-bigquery-project
DATASET_ID=mimiciv_hosp
MAX_PATIENTS=1000
CACHE_TTL=3600

📈 Usage Examples

Loading Patient Data

from mimic_client import MIMICClient

# Initialize client
client = MIMICClient(project_id='your-project')

# Extract oncology cohort
oncology_data = client.extract_oncology_cohort(limit=100)
patients_df = oncology_data['patients']
events_df = oncology_data['events']

Digital Twin Matching

# Find similar patients
similar_patients = find_digital_twins(
    age=65,
    gender='M',
    cancer_type='Colorectal Cancer',
    stage='T3N1M0'
)

🏗️ Architecture

┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐
│   Streamlit UI  │────▶│ BigQuery API │────▶│  MIMIC-IV Data  │
└─────────────────┘     └──────────────┘     └─────────────────┘
         │                      │
         ▼                      ▼
┌─────────────────┐     ┌──────────────┐
│  Visualizations │     │   Analytics  │
└─────────────────┘     └──────────────┘

🧪 Testing

# Run all tests
python -m pytest

# Run with coverage
python -m pytest --cov=.

# Quick connection test
python quick_test.py

📝 Documentation

• API Reference
• Data Schema
• User Guide

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📊 Performance

• Processes 1000 patients in ~5 seconds
• Supports cohorts up to 10,000 patients
• Real-time filtering and visualization
• Cached queries for improved performance

🔒 Security & Privacy

• No PHI downloaded to local machine
• All processing done in BigQuery
• Compliant with MIMIC-IV data use agreement
• Session-based authentication

📚 Citation

If you use this project in your research, please cite:

@software{mimic_oncology_pathways,
  author = {Mirzafarangi},
  title = {MIMIC-IV Oncology Pathway Mapping Engine},
  year = {2024},
  url = {https://github.com/mirzafarangi/mimic-oncology-bigquery-git}
}

📄 License

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

🙏 Acknowledgments

• MIMIC-IV dataset and PhysioNet team
• Streamlit for the amazing framework
• Google BigQuery for scalable data processing

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