brainsait-pybrain 0.1.0

Unified Healthcare Intelligence Platform - AI-powered healthcare data harmonization and decision support

🧠 PyBrain - Unified Healthcare Intelligence Platform

PyBrain is the intelligence layer of the BrainSAIT Healthcare Unification Platform, providing AI-powered data harmonization, clinical NLP, and decision support for building next-generation healthcare systems.

🚀 Features

• AI-Powered Data Harmonization: Automatically maps and transforms data across different healthcare standards
• Clinical NLP Engine: Extracts structured data from unstructured clinical notes with medical language understanding
• Federated Learning Framework: Enables privacy-preserving AI model training across healthcare institutions
• Real-time Decision Support: Provides evidence-based recommendations using ensemble AI models
• Predictive Analytics: Forecasts patient outcomes, resource needs, and population health trends

📦 Installation

pip install pybrain

For development:

pip install pybrain[dev]

For all ML features:

pip install pybrain[ml,nlp]

🔧 Quick Start

Basic Usage

from pybrain import AIEngine, DataHarmonizer

# Initialize AI engine
ai = AIEngine()

# Extract entities from clinical text
clinical_note = "Patient presents with type 2 diabetes, prescribed metformin 500mg twice daily"
entities = ai.extract_clinical_entities(clinical_note)
print(entities)
# {'conditions': ['Diabetes'], 'medications': ['Metformin'], ...}

# Harmonize HL7v2 data to FHIR
harmonizer = DataHarmonizer()
hl7_data = {
    "PID": {
        "5": {"1": "Smith", "2": "John"},
        "7": "19800415",
        "8": "M"
    }
}
fhir_patient = harmonizer.harmonize_to_fhir(hl7_data, "hl7v2", "Patient")

AI-Powered Risk Assessment

from pybrain import AIEngine, DecisionEngine

ai = AIEngine()
decision_engine = DecisionEngine()

# Patient data
patient_data = {
    "age": 65,
    "conditions": ["diabetes", "hypertension"],
    "medications": ["metformin", "lisinopril"],
    "bmi": 28.5
}

# Predict clinical risks
risk_score = ai.predict_risk_score(patient_data)
print(f"Overall risk score: {risk_score:.2f}")

# Get clinical recommendations
recommendations = decision_engine.evaluate_patient(patient_data)
print("Clinical alerts:", recommendations["alerts"])

Population Health Analytics

from pybrain import AnalyticsEngine

analytics = AnalyticsEngine()

# Analyze population trends
population_data = [
    {"patient": {"id": "1", "birthDate": "1960-01-01"}, "observations": [...]},
    {"patient": {"id": "2", "birthDate": "1975-05-15"}, "observations": [...]}
]

metrics = analytics.calculate_population_metrics(population_data)
print(f"High-risk patients: {metrics['risk_distribution']['high']}")
print(f"Recommendations: {metrics['recommendations']}")

CLI Usage

# Analyze clinical text
pybrain analyze -t "Patient has hypertension and diabetes"

# Harmonize data files
pybrain harmonize -i patient.json -f hl7v2 -r Patient -o patient_fhir.json

# Start API server
pybrain serve --port 8000

🏗️ Architecture

PyBrain is designed as a modular, scalable platform:

pybrain/
├── core/
│   ├── ai/          # AI models and engines
│   ├── harmonizer/  # Data harmonization
│   ├── analytics/   # Analytics engine
│   ├── decision/    # Decision support
│   └── knowledge/   # Knowledge graphs
├── connectors/      # External system connectors
├── models/          # Pre-trained models
└── utils/          # Utilities

🤝 Integration with PyHeart

PyBrain works seamlessly with PyHeart for complete healthcare system unification:

from pybrain import AIEngine
from pyheart import FHIRClient

# Use PyHeart for data access
client = FHIRClient("https://fhir.example.com")
patient_data = client.get_patient("12345")

# Use PyBrain for intelligence
ai = AIEngine()
risk_score = ai.predict_risk_score(patient_data)

if risk_score > 0.8:
    print("High-risk patient - immediate intervention required")

🧪 Key Capabilities

Clinical NLP

• Medical entity extraction
• Clinical concept normalization
• FHIR-compliant text processing
• Multi-language support

AI-Powered Analytics

• Risk stratification
• Readmission prediction
• Fall risk assessment
• Medication adherence prediction

Data Harmonization

• HL7v2 to FHIR transformation
• Custom EHR format mapping
• Terminology services integration
• Quality validation

Decision Support

• Clinical rule engine
• Evidence-based recommendations
• Drug interaction checking
• Population health insights

📚 Documentation

Full documentation available at: https://pybrain.readthedocs.io

🧪 Testing

# Run tests
pytest

# With coverage
pytest --cov=pybrain

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

PyBrain is licensed under the Apache License 2.0. See LICENSE for details.

🌟 Acknowledgments

Built with ❤️ by the BrainSAIT Healthcare Innovation Lab

Special thanks to the open-source healthcare community and all contributors.

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Together with PyHeart, PyBrain is building the future of intelligent healthcare.