Advanced Multimodal Deep Learning Framework for Integrated Clinical Risk Assessment
Cortexa is a pioneering, unified multimodal deep learning framework designed to revolutionize clinical diagnostics by seamlessly integrating disparate biomedical data streams. Unlike traditional siloed diagnostic approaches, Cortexa recognizes and leverages the intricate synergistic relationships between different physiological systems.
The platform enables simultaneous prediction of multiple clinically relevant conditions through a state-of-the-art deep neural network architecture deployed across a modern, decoupled microservice infrastructure. This innovative approach promises earlier disease detection, more personalized risk stratification, and ultimately, improved patient outcomes.
- Multimodal Data Integration: Seamlessly fuses physiological time-series (ECG, SpO2, HRV, EDA, TEMP) with medical imaging (MRI, CT)
- Simultaneous Multi-Condition Prediction: Predicts 5+ clinically relevant conditions concurrently
- Automated Clinical Narratives: Transforms complex ML outputs into human-readable clinical reports
- Real-Time Clinical Dashboard: Live visualization of patient signals and predictive alerts
- Enterprise-Grade Security: HIPAA-compliant, end-to-end encryption (AES-256), SHA-256 data integrity
- Scalable Microservices: Modern distributed architecture supporting horizontal scaling
- Team-Based ML Pipeline: Five specialized teams optimized for specific pathologies
Cortexa predicts and monitors the following conditions across diverse patient populations:
| Condition | Primary Modalities | Team | Status |
|---|---|---|---|
| Sleep Apnea & Stress | SpO2, HRV, EDA, TEMP, ACC | Team 1 | In Development |
| Anemia & Diabetes Risk | SpO2, HR, Activity, Sleep | Team 2 | In Development |
| Atrial Fibrillation | ECG, HR, BVP | Team 3 | In Development |
| Burnout & Overtraining | HRV Trends, Activity, Sleep | Team 4 | In Development |
| Brain Tumor Identification | MRI/CT Images | Team 5 | In Development |
┌──────────────────────────────────────────────────────────────────────────┐
│ Clinical User Interface │
│ Next.js Web App (React 19 + TypeScript) │
└────────────────────────┬─────────────────────────────────────────────────┘
│ HTTPS
┌────────────────▼────────────────────────┐
│ Express.js Orchestrator (API Gateway) │
│ • Authentication & RBAC │
│ • Load Balancing & Routing │
│ • Database Interface │
│ • Alert Management │
└──────────┬─────────────┬────────────────┘
│ │
┌──────────▼──┐ ┌──────▼─────────────┐
│ FastAPI │ │ Encryption API │
│ Inference │ │ • AES-256 │
│ Engine │ │ • SHA-256 Hash │
│ (ML Core) │ │ • Key Management │
└──────────┬──┘ └────────────────────┘
│
┌──────────▼──────────┐
│ PostgreSQL DB │
│ (HIPAA-Compliant) │
└─────────────────────┘
For detailed architecture documentation, see ARCHITECTURE.md
- Python: 3.11+
- Node.js: 20+ (LTS)
- Docker: Latest
- Docker Compose: Latest
- PostgreSQL: 16+ (if running without Docker)
# Clone the repository
git clone https://github.com/your-org/cortexa.git
cd cortexa
# Build all services
docker-compose build
# Start all services
docker-compose up -d
# View logs
docker-compose logs -f
# Verify all services are running
curl http://localhost:3000 # Next.js Frontend
curl http://localhost:3000/api # Express API
curl http://localhost:8000 # FastAPI InferenceAccess the application:
- Frontend: http://localhost:3000
- API Documentation: http://localhost:8000/docs (FastAPI Swagger UI)
- Express API: http://localhost:3000/api
cd apps/web-app
# Install dependencies
npm install
# Start development server (http://localhost:3000)
npm run dev
# Run linting
npm run lintcd services/express-api
# Install dependencies
npm install
# Create .env.local with your configuration
cp .env.example .env.local
# Start development server
npm run devcd services/fast-api
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Start development server
uvicorn app.main:app --reload| Document | Purpose |
|---|---|
| ARCHITECTURE.md | Detailed system architecture, data flows, deployment guide |
| API_CONTRACTS.md | OpenAPI specifications, endpoint documentation |
| ML_PIPELINE.md | ML methodology, team-specific architectures, datasets |
| ENCRYPTION.md | Security protocols, encryption implementation, compliance |
| DEPLOYMENT.md | Production deployment procedures, troubleshooting |
- Next.js 16.1.6 - React framework with SSR
- React 19 - UI library
- TypeScript 5 - Type safety
- Tailwind CSS 4 - Utility-first styling
- Express.js - Node.js API framework
- FastAPI - Python asynchronous web framework
- PyTorch - Deep learning framework
- PyTorch - Neural network implementation
- NumPy/SciPy - Numerical computing
- Pandas - Data manipulation
- librosa - Signal processing
- Scikit-learn - ML utilities
- PostgreSQL - Relational database
- Docker - Containerization
- Docker Compose - Local orchestration
- Kubernetes (future) - Production orchestration
- Streamlit - Interactive dashboards
- Jupyter - Exploratory analysis
- Matplotlib/Plotly - Data visualization
cortexa/
├── apps/
│ ├── web-app/ # Next.js clinical interface
│ └── mobile-app/ # Mobile client (future)
├── services/
│ ├── express-api/ # API orchestrator & gateway
│ ├── fast-api/ # ML inference engine
│ └── encrypt-api/ # Encryption service
├── ml/
│ ├── pipelines/ # Data preprocessing
│ ├── training/ # Model training
│ ├── experiments/ # Team research notebooks
│ ├── models/ # Trained model artifacts
│ └── config.yaml # ML configuration
├── analytics/
│ ├── dashboards/ # Streamlit application
│ └── notebooks/ # Analysis notebooks
├── infra/
│ ├── docker/ # Dockerfile definitions
│ └── scripts/ # Deployment automation
├── docs/ # Documentation
└── shared/ # Shared types & utilities
For detailed directory structure, see ARCHITECTURE.md
git checkout -b feature/your-feature-namegit add .
git commit -m "[feat]: Brief description of your changes"git push origin feature/your-feature-name
# Create PR on GitHub with detailed description- At least 2 approvals required
- All automated tests must pass
- Documentation updated
[TYPE]: Short description (50 chars max)
Detailed explanation of intentional changes and rationale.
Link related issues with #123.
- Bullet points for complex changes
- Another point if needed
Types: feat fix docs style refactor test chore security
✅ End-to-End Encryption
- AES-256-GCM for data at rest in PostgreSQL
- TLS 1.3 for all network communication
- Unique IVs for each encryption operation
✅ Data Integrity
- SHA-256 HMAC for all clinical records
- Tamper-evident logging
- Immutable audit trails
✅ HIPAA Compliance
- Protected Health Information (PHI) encryption
- Role-Based Access Control (RBAC)
- Comprehensive audit logging
- Business Associate Agreements (BAA)
✅ Access Control
- Multi-role support (admin, clinician, technician)
- Token-based authentication (JWT)
- Session management with automatic timeout
- Multi-factor authentication (optional, future)
- Never commit secrets - Use environment variables
- Validate all inputs - Express & FastAPI validators
- Use parameterized queries - SQL injection prevention
- Keep dependencies updated - Regular security patches
- Report vulnerabilities responsibly - security@cortexa.clinical
# Frontend tests
cd apps/web-app && npm test
# Express API tests
cd services/express-api && npm test
# FastAPI tests
cd services/fast-api && pytest
# ML pipeline tests
cd ml && pytest# Create migration (Express backend)
cd services/express-api && npm run migrate:create -- --name "add_new_table"
# Run migrations
docker-compose exec express npm run migrate:upcd ml
# Preprocessing
python pipelines/preprocess.py --input raw_data/ --output processed_data/
# Feature engineering
python pipelines/feature_engineering.py --input processed_data/ --output features/
# Model training
python training/train.py --config config.yaml --output models/# Build all services
docker-compose build
# Build specific service
docker-compose build express-api
# Build with custom tag
docker build -t cortexa/express-api:v2.0 -f infra/docker/express.Dockerfile .# All services
docker-compose logs -f
# Specific service
docker-compose logs -f express-api
# Last 100 lines of specific service
docker-compose logs --tail=100 fastapiExpress API:
DEBUG=cortexa:* npm run devFastAPI:
uvicorn app.main:app --reload --log-level debugWe welcome contributions from developers, clinicians, and researchers. Please see CONTRIBUTING.md for detailed guidelines.
- Report Issues: Use GitHub issues with detailed reproduction steps
- Suggest Features: Discuss in issues before working on implementation
- Submit Code: Follow the development workflow above
- Improve Documentation: Typos, clarity, examples all appreciated
- Share Findings: Research discoveries and insights
- TypeScript: ESLint config enforced, strict mode
- Python: PEP 8, type hints via mypy
- Documentation: Comprehensive docstrings and README updates
- Testing: >80% code coverage for new features
- Documentation: Check ARCHITECTURE.md and guides
- GitHub Issues: Report bugs and request features
- Security Issues: Email security@cortexa.clinical (confidential)
- Clinical Questions: Contact clinical-team@cortexa.clinical
| Role | Contact |
|---|---|
| Project Lead | [Ishan Dwivedi] |
| Frontend Engineer | [Anshika Bharadwaj] |
| Backend Developer (Fast API) | [Ayush Raj] |
| Backend Engineer (Express API) | [Anurag Pandey (2301640100101)] |
| DevOps Engineer | [Anurag Pandey (2301640100100)] |
- Slack Channel: #cortexa (internal team)
- Discussion Forum: GitHub Discussions
- Weekly Standup: Tuesdays 10 AM (internal)
- Database schema finalization
- API contract definition
- Security architecture review
- Frontend scaffolding
- Express orchestrator setup
- FastAPI initialization
- Model loading and baseline inference
- Team-specific model training
- Feature fusion implementation
- NLG report generation
- Alert system deployment
- Encryption implementation
- Digital fingerprinting
- HIPAA compliance audit
- High-concurrency testing
- UI/UX polish
- Performance optimization
- Production readiness
See ARCHITECTURE.md for detailed sprint breakdown
MIT License
If you use Cortexa in your research, please cite:
@software{cortexa2026,
title={Cortexa: Multimodal Deep Learning Framework for Integrated Clinical Risk Assessment},
author={[Your Team]},
year={2026},
url={https://github.com/your-org/cortexa}
}- Clinical Advisors: [Names and institutions]
- Dataset Providers: PhysioNet, Kaggle, MIT-BIH
- Open Source Community: PyTorch, FastAPI, Next.js teams
- Research Collaborators: [Institutions]
- Complete architecture redesign with team-based ML pipeline
- Multi-modal signal fusion implementation
- HIPAA-compliant security layer
- Real-time clinical dashboard
- Automated clinical report generation
- Initial project setup
- Basic API scaffolding
- Baseline ML models
Q: How accurate are the predictions? A: Each model is trained to >95% accuracy on validation sets. Clinical performance will be validated during independent testing phases.
Q: Can I use my own medical data? A: The system supports DICOM images and standard physiological signal formats. Data must be de-identified and follow HIPAA guidelines.
Q: Is this FDA approved? A: Cortexa is currently in development. FDA 510(k) clearance is planned for the future.
Q: Can I deploy this on my own servers? A: Yes, Docker Compose and Kubernetes deployment options are available. See DEPLOYMENT.md.
Q: How is my data encrypted? A: All data uses AES-256-GCM encryption at rest and TLS 1.3 in transit. See ENCRYPTION.md for details.
Q: What's the system uptime? A: We target 99.9% uptime with automatic failover and disaster recovery procedures.
Last Updated: February 28, 2026
Maintainers: [Team]
Status: Active Development
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Cortexa - Bridging Advanced AI and Clinical Care