proofly 1.1.2

A package for health metric analysis and prediction

Proofly

Enterprise-grade health metrics analysis and prediction engine for healthcare applications

Proofly is a production-ready Python package that empowers healthcare applications with advanced analytics and predictive capabilities. Built with a focus on accuracy and reliability, it processes vital health metrics to deliver actionable insights for various chronic conditions including diabetes, hypertension, COPD, CKD, and CHF.

Table of Contents

• Features
• Installation
• Quick Start
• Usage Guide
  • Basic Usage
  • Advanced Features
  • Configuration
  • Error Handling
• API Reference
• Contributing
• License
• Support

Features

Core Capabilities

• Comprehensive Health Scoring

  • Evidence-based scoring algorithms
  • Condition-specific metric weighting
  • Customizable scoring models
  • Real-time score calculation

• Clinical Risk Assessment

  • Multi-factor risk stratification
  • Predictive risk modeling
  • Early warning system
  • Risk trend analysis

• Longitudinal Analysis

  • Time-series health data processing
  • Trend detection and forecasting
  • Statistical significance testing
  • Anomaly detection

• Smart Clinical Recommendations

  • Personalized health insights
  • Evidence-based intervention suggestions
  • Lifestyle modification recommendations
  • Medication adherence tracking

Technical Features

• Robust input validation with comprehensive error handling
• Thread-safe implementation for concurrent processing
• Efficient caching system for improved performance
• Extensive logging and monitoring capabilities
• HIPAA-compliant data handling
• RESTful API integration support
• Exportable reports in multiple formats
• Customizable alert thresholds

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager
• Virtual environment (recommended)

Standard Installation

pip install proofly

Development Installation

# Clone the repository
git clone https://github.com/moroii69/proofly-python-package.git
cd proofly

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

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

Optional Dependencies

# Install with all optional features
pip install "proofly[all]"

# Install specific feature sets
pip install "proofly[visualization]"  # For plotting capabilities
pip install "proofly[export]"         # For report export features
pip install "proofly[ml]"             # For advanced ML features

Quick Start

from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics
from datetime import datetime

# Initialize the analyzer
analyzer = HealthAnalyzer(
    config={
        "logging_level": "INFO",
        "cache_enabled": True,
        "validation_mode": "strict"
    }
)

# Create metrics using the type-safe model
metrics = DiabetesMetrics(
    blood_glucose=120,
    hba1c=6.5,
    blood_pressure=130,
    timestamp=datetime.now()
)

# Perform analysis
result = analyzer.analyze_metrics(
    condition="diabetes",
    metrics=metrics
)

# Access results
print(f"Health Score: {result.health_score}")
print(f"Risk Level: {result.risk_level.name}")
print(f"Confidence: {result.confidence_score}%")
print("\nRecommendations:")
for rec in result.recommendations:
    print(f"- {rec.description}")

Usage Guide

Basic Usage

Supported Health Conditions

Proofly provides specialized analysis for multiple chronic conditions:

1. Diabetes Management

from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics

analyzer = HealthAnalyzer()

# Single point analysis
diabetes_metrics = DiabetesMetrics(
    blood_glucose=120,  # mg/dL
    hba1c=6.5,         # %
    blood_pressure=130  # mmHg
)

result = analyzer.analyze_metrics(
    condition="diabetes",
    metrics=diabetes_metrics
)

# Access comprehensive results
print(f"Health Score: {result.health_score}")
print(f"Risk Level: {result.risk_level.name}")
print(f"Confidence: {result.confidence_score}%")

# Get detailed analysis
analysis = result.get_detailed_analysis()
print(f"Blood Glucose Status: {analysis.blood_glucose.status}")
print(f"HbA1c Trend: {analysis.hba1c.trend}")
print(f"Blood Pressure Category: {analysis.blood_pressure.category}")

2. Hypertension Monitoring

from proofly.models import HypertensionMetrics

hypertension_metrics = HypertensionMetrics(
    systolic_pressure=130,   # mmHg
    diastolic_pressure=85,   # mmHg
    heart_rate=72           # bpm
)

result = analyzer.analyze_metrics(
    condition="hypertension",
    metrics=hypertension_metrics
)

3. COPD Assessment

from proofly.models import COPDMetrics

copd_metrics = COPDMetrics(
    oxygen_saturation=95,    # %
    peak_flow=350,          # L/min
    respiratory_rate=18      # breaths/min
)

result = analyzer.analyze_metrics(
    condition="copd",
    metrics=copd_metrics
)

Advanced Features

Historical Analysis

from proofly.models import HistoricalData

# Create historical dataset
historical_data = HistoricalData([
    DiabetesMetrics(
        blood_glucose=118,
        hba1c=6.4,
        blood_pressure=128,
        timestamp=datetime(2024, 1, 1)
    ),
    DiabetesMetrics(
        blood_glucose=122,
        hba1c=6.5,
        blood_pressure=130,
        timestamp=datetime(2024, 1, 2)
    )
])

# Analyze with historical context
result = analyzer.analyze_metrics(
    condition="diabetes",
    metrics=diabetes_metrics,
    history=historical_data,
    analysis_options={
        "trend_analysis": True,
        "prediction_window": "30d",
        "confidence_threshold": 0.85
    }
)

# Access trend analysis
trend = result.get_trend_analysis()
print(f"Trend Direction: {trend.direction}")
print(f"Rate of Change: {trend.rate_of_change}")
print(f"Prediction (30 days): {trend.prediction.value}")

Custom Analysis Configuration

from proofly.config import AnalysisConfig
from proofly.enums import ValidationMode, RiskModel

# Configure analysis parameters
config = AnalysisConfig(
    validation_mode=ValidationMode.STRICT,
    risk_model=RiskModel.CONSERVATIVE,
    confidence_threshold=0.9,
    cache_enabled=True,
    include_raw_data=True
)

# Initialize analyzer with custom configuration
analyzer = HealthAnalyzer(config=config)

# Perform analysis with custom options
result = analyzer.analyze_metrics(
    condition="diabetes",
    metrics=diabetes_metrics,
    options={
        "detailed_analysis": True,
        "include_recommendations": True,
        "prediction_enabled": True
    }
)

Export and Reporting

from proofly.export import ReportGenerator
from proofly.enums import ReportFormat

# Generate detailed report
report = ReportGenerator.create_report(
    result,
    format=ReportFormat.PDF,
    include_graphs=True,
    include_recommendations=True
)

# Export report
report.save("health_analysis_report.pdf")

# Export raw data
result.export_data("analysis_data.json")

Error Handling

from proofly.exceptions import (
    ValidationError,
    ConfigurationError,
    AnalysisError
)

try:
    result = analyzer.analyze_metrics(
        condition="diabetes",
        metrics=DiabetesMetrics(
            blood_glucose=500,  # Invalid value
            hba1c=6.5,
            blood_pressure=130
        )
    )
except ValidationError as e:
    print(f"Validation Error: {e.message}")
    print(f"Invalid Fields: {e.invalid_fields}")
except ConfigurationError as e:
    print(f"Configuration Error: {e.message}")
except AnalysisError as e:
    print(f"Analysis Error: {e.message}")
    print(f"Error Code: {e.error_code}")

Configuration

Metric Ranges and Thresholds

from proofly.config import MetricConfig

# Access default configurations
diabetes_config = MetricConfig.get_condition_config("diabetes")
print("Normal Ranges:")
print(f"Blood Glucose: {diabetes_config.ranges.blood_glucose.normal}")
print(f"HbA1c: {diabetes_config.ranges.hba1c.normal}")
print(f"Blood Pressure: {diabetes_config.ranges.blood_pressure.normal}")

# Customize metric thresholds
custom_config = MetricConfig(
    condition="diabetes",
    ranges={
        "blood_glucose": {
            "normal": (80, 120),
            "warning": (120, 140),
            "critical": (140, 200)
        }
    }
)

analyzer.update_config(custom_config)

API Reference

Core Classes

• HealthAnalyzer: Main analysis engine
• MetricConfig: Configuration management
• AnalysisResult: Analysis results container
• ReportGenerator: Report generation utility

Models

• DiabetesMetrics: Diabetes-specific metrics
• HypertensionMetrics: Hypertension-specific metrics
• COPDMetrics: COPD-specific metrics
• CKDMetrics: CKD-specific metrics
• CHFMetrics: CHF-specific metrics

Utility Classes

• TrendAnalyzer: Time-series analysis tools
• RiskCalculator: Risk assessment utilities
• RecommendationEngine: Recommendation generation
• DataValidator: Input validation tools

Contributing

We welcome contributions! Please follow these steps:

1. Check the Issues page for open tasks
2. Fork the repository
3. Create a feature branch (git checkout -b feature/amazing-feature)
4. Make your changes with appropriate tests
5. Run the test suite (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to your branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Development Setup

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

# Run tests
pytest

# Run linting
flake8 proofly
black proofly

# Generate documentation
cd docs
make html

License

Distributed under the MIT License. See LICENSE for more information.

Support

• Issue Tracker: GitHub Issues
• Email Support: [e-mail]

Acknowledgments

• Built with input from healthcare professionals
• Implements evidence-based medical guidelines
• Uses validated statistical models
• Follows healthcare industry best practices
• Adheres to HIPAA compliance standards

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Made with ❤️ by the Proofly Team