dop1 0.1.0

Diabetes Osteoporosis Prediction Package - AI-powered analysis for diabetes complicated with osteoporosis

DOP1 - Diabetes Osteoporosis Prediction Package

A Python package for AI-powered analysis of diabetes complicated with osteoporosis using machine learning and OpenAI's GPT models.

Features

• AI-Powered Analysis: Uses OpenAI's GPT models for intelligent prediction of osteoporosis risk in diabetic patients
• Comprehensive Data Processing: Handles laboratory and clinical feature data
• Batch Processing: Efficiently processes large datasets with progress tracking
• Error Handling: Robust error handling with detailed logging
• Configurable API: Flexible API endpoint and key configuration
• Command Line Interface: Easy-to-use CLI for batch processing
• Comprehensive Testing: Full test suite with coverage reporting

Installation

From Source (Development)

# Clone the repository
git clone https://github.com/dop-research/dop1.git
cd dop1

# Install in development mode
pip install -e .

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

# Run tests
make test

Using pip (when published)

pip install dop1

Quick Start

Python API

from dop1 import DOPPredictor

# Initialize the predictor
predictor = DOPPredictor(api_key="your-api-key")

# Analyze a single patient
patient_data = {
    'id': 1000529,
    'GROUP': 1,  # This will be predicted
    'Age': 1,
    'Gender': 0,
    'ALP': 0,
    'BMI': 1,
    'GNRI': 1,
    'eGFR': 0,
    'SII': 1,
    'FT4': 0,
    'PDW': 0,
    'Creatinine': 0,
    'FT3': 0,
    'RDW_SD': 1
}

result = predictor.predict_single(patient_data)
print(f"Prediction: {result}")

# Process a batch of patients from CSV
predictor.process_batch("patients.csv", output_dir="results")

Command Line Interface

# Set API key
export OPENAI_API_KEY="your-api-key-here"

# Process a CSV file
dop1 predict data.csv --output results/

# Use custom model
dop1 predict data.csv --model gpt-4 --output results/

# Show help
dop1 --help

Data Format

The package expects CSV files with the following columns:

Column	Description	Values
id	Patient identifier	Any unique identifier
GROUP	Target variable	0 = low bone mass/normal, 1 = osteoporosis/severe osteoporosis
Age	Age group	0 or 1
Gender	Gender	0 = male, 1 = female
ALP	Alkaline phosphatase	0 or 1
BMI	Body Mass Index	0 or 1
GNRI	Geriatric Nutritional Risk Index	0 or 1
eGFR	Estimated Glomerular Filtration Rate	0 or 1
SII	Systemic Immune-Inflammation Index	0 or 1
FT4	Free Thyroxine	0 or 1
PDW	Platelet Distribution Width	0 or 1
Creatinine	Serum Creatinine	0 or 1
FT3	Free Triiodothyronine	0 or 1
RDW_SD	Red Cell Distribution Width	0 or 1

Configuration

Environment Variables

export OPENAI_API_KEY="your-api-key-here"
export OPENAI_BASE_URL="https://api.ocoolai.com/v1"  # Optional
export DOP1_MODEL="gpt-5"  # Optional

Programmatic Configuration

predictor = DOPPredictor(
    api_key="your-api-key",
    base_url="https://api.ocoolai.com/v1",
    model="gpt-5"
)

Examples

Basic Usage

See examples/basic_usage.py for a complete example.

Sample Data

Use examples/sample_data.csv as a template for your data format.

Development

Running Tests

# Run all tests
make test

# Run tests without coverage
make test-fast

# Run specific test file
pytest tests/test_predictor.py -v

Code Quality

# Format code
make format

# Run linting
make lint

# Run all checks
make check

Building

# Build package
make build

# Clean build artifacts
make clean

API Reference

DOPPredictor Class

__init__(api_key, base_url, model)

Initialize the predictor with API configuration.

predict_single(patient_data)

Predict osteoporosis risk for a single patient.

process_batch(filepath, output_dir, skip_existing)

Process a batch of patients from CSV file.

get_model_info()

Get information about the current model configuration.

Utility Functions

load_data(filepath)

Load patient data from CSV file.

validate_data(data)

Validate patient data format.

save_results(results, filepath)

Save results to file.

Error Handling

The package provides custom exceptions:

• DOPError: Base exception class
• ValidationError: Data validation errors
• APIError: API call failures
• ConfigurationError: Configuration issues
• FileError: File operation errors

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

• Create an issue on GitHub
• Check the documentation
• Review the examples

Changelog

v0.1.0

• Initial release
• Basic prediction functionality
• Batch processing
• CLI interface
• Comprehensive testing