🏥 ProbableDoctor

Enhanced Medical Professional Name Parser with advanced multi-credential support.

ProbableDoctor is an enhanced Python library for parsing medical professional names with proper handling of multiple comma-separated credentials and titles. Built on top of the robust probablepeople library, it provides specialized parsing for healthcare professionals.

✨ Key Features

• 🎓 Multi-Credential Parsing: Properly handles multiple credentials like "MD, PhD, FACP"
• 👨‍⚕️ Medical Titles: Recognizes medical prefixes and professional titles
• 📋 Structured Output: Returns parsed components as dictionaries for easy processing
• 🔄 Backwards Compatible: Works with existing probablepeople code
• 🎯 High Accuracy: Uses advanced NLP and machine learning

🚀 Quick Start

Installation

pip install probabledoctor

Basic Usage

import probabledoctor

# Parse a medical professional's name
result, name_type = probabledoctor.tag("Dr. Sarah Johnson MD, PhD, FACP")

print(result)
# Output: {
#     'PrefixOther': 'Dr.',
#     'GivenName': 'Sarah', 
#     'Surname': 'Johnson',
#     'SuffixOther': 'MD, PhD, FACP'
# }

print(f"Name type: {name_type}")
# Output: Name type: Person

CLI Usage

# Parse and tag a name
probabledoctor "Dr. Jane Doe MD" --tag

# Parse without tagging
probabledoctor "Dr. Jane Doe MD"

# Use specific model type
probabledoctor "Smith Medical Corp" --type company --tag

📊 Examples

Multiple Credentials

import probabledoctor

names = [
    "Taylor Anne Jordan ATC, LAT",
    "Dr. Sarah Johnson MD, PhD, FACP", 
    "Michael Smith RN, BSN, CEN",
    "David Wilson PhD, MD"
]

for name in names:
    result, name_type = probabledoctor.tag(name)
    credentials = result.get('SuffixOther', '')
    full_name = f"{result.get('GivenName', '')} {result.get('Surname', '')}"
    
    print(f"👤 {full_name}")
    print(f"🎓 Credentials: {credentials}")
    print(f"📋 Type: {name_type}")
    print()

Output:

👤 Taylor Jordan
🎓 Credentials: ATC, LAT
📋 Type: Person

👤 Sarah Johnson  
🎓 Credentials: MD, PhD, FACP
📋 Type: Person

👤 Michael Smith
🎓 Credentials: RN, BSN, CEN
📋 Type: Person

👤 David Wilson
🎓 Credentials: PhD, MD
📋 Type: Person

Backwards Compatibility

ProbableDoctor maintains full compatibility with probablepeople:

import probabledoctor as pp

# Use standard probablepeople functions
result = pp.parse("John Smith")
tagged = pp.tag("Dr. Jane Doe MD")

🏗️ What's Enhanced?

⚠️ Known Limitations

• Parsing of trailing initials: In some cases, names with a trailing initial (e.g., "John Doe A") might have the initial misclassified. For example, running probabledoctor "Kashani Jamshid A" --tag may currently identify "A" as SuffixOther instead of a MiddleInitial or as part of the main name components. This is related to the intricacies of the statistical model used for parsing. Efforts to improve accuracy for these patterns are ongoing.

🎯 Use Cases

• Healthcare Systems: Parse doctor names from databases
• Medical Records: Extract credentials from patient records
• Research: Analyze medical professional credentials
• HR Systems: Process healthcare worker information
• Compliance: Verify medical professional credentials

📚 Advanced Usage

Custom Model Types

# Use different parsing models
result = probabledoctor.tag("Dr. Smith", type="person")
result = probabledoctor.tag("Smith Medical Corp", type="company")

Integration with Existing Code

# Drop-in replacement for probablepeople
import probabledoctor as pp

# Your existing probablepeople code works unchanged
names = ["John Smith", "Dr. Jane Doe MD"]
for name in names:
    parsed = pp.parse(name)
    tagged = pp.tag(name)

Requirements

• Python 3.9+
• python-crfsuite>=0.7
• probableparsing
• doublemetaphone

Installation from Source

git clone https://github.com/atk81-candor/probabledoctor
cd probabledoctor
pip install -e .

Running Tests

pytest tests/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🆘 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

🙏 Acknowledgments

Built on top of the excellent probablepeople library by DataMade.

Made with ❤️ for the healthcare community