Proofly

Enterprise-grade health metrics analysis and prediction engine for healthcare applications. Proofly empowers healthcare applications with advanced analytics and predictive capabilities, focusing on chronic conditions including diabetes, hypertension, COPD, and more.

Table of Contents

• Features
• Installation
• Dependencies
• Quick Start
• Usage Guide
  • Supported Health Conditions
  • Exporting Results
  • Error Handling
• License
• Support
• Acknowledgments

Features

• Comprehensive health scoring with evidence-based algorithms
• Multi-factor risk stratification
• Time-series health data processing
• Smart clinical recommendations
• HIPAA-compliant data handling
• Extensive error handling and validation
• Export capabilities for reports and analysis

Installation

pip install proofly

Dependencies

• Python ≥ 3.8
• dataclasses
• typing
• datetime

Quick Start

from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics

# Initialize analyzer
analyzer = HealthAnalyzer()

# Create metrics
metrics = DiabetesMetrics(
    blood_glucose=120,  # mg/dL
    hba1c=6.5,         # %
    blood_pressure=130  # mmHg
)

# Analyze metrics
result = analyzer.analyze_metrics("diabetes", metrics)

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

Usage Guide

Supported Health Conditions

Diabetes Management

from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics

analyzer = HealthAnalyzer()

diabetes_metrics = DiabetesMetrics(
    blood_glucose=120,
    hba1c=6.5,
    blood_pressure=130
)

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

analysis = result.get_detailed_analysis()
print(f"Health Score: {analysis['health_score']}")
print(f"Risk Level: {analysis['risk_level']}")

Hypertension Monitoring

from proofly import HealthAnalyzer
from proofly.models import HypertensionMetrics

analyzer = HealthAnalyzer()
hypertension_metrics = HypertensionMetrics(
    systolic_pressure=130,
    diastolic_pressure=85,
    heart_rate=72
)
result = analyzer.analyze_metrics("hypertension", hypertension_metrics)
print(f"Health Score: {result.health_score}")

COPD Assessment

from proofly.models import COPDMetrics

copd_metrics = COPDMetrics(
    oxygen_saturation=95,
    peak_flow=350,
    respiratory_rate=18
)
result = analyzer.analyze_metrics("copd", copd_metrics)
print(f"Health Score: {result.health_score}")

Exporting Results

# Export Example
from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics
from proofly.export import ReportGenerator
from proofly.enums import ReportFormat

analyzer = HealthAnalyzer()
metrics = DiabetesMetrics(blood_glucose=120, hba1c=6.5, blood_pressure=130)
result = analyzer.analyze_metrics("diabetes", metrics)

report = ReportGenerator.create_report(
    result,
    format=ReportFormat.PDF,
    include_graphs=True,
    include_recommendations=True
)
print(report['data'])

Error Handling

from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics
from proofly.exceptions import ValidationError, ConfigurationError, AnalysisError

analyzer = HealthAnalyzer()
try:
    result = analyzer.analyze_metrics(
        condition="diabetes",
        metrics=DiabetesMetrics(
            blood_glucose=500,
            hba1c=6.5,
            blood_pressure=130
        )
    )
except ValidationError as e:
    print(f"Validation Error: {e.message}")

License

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

Support

• Issue Tracker: GitHub Issues
• Email Support: support@proofly.xyz

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