mcs 0.0.3

Paper - Pytorch

Medical Diagnosis Swarm Architecture

A production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents.

Installation

pip install mcs

Usage

from mcs.main import MedicalCoderSwarm
import json

if __name__ == "__main__":
  # Example patient case
  patient_case = """
  Patient: 45-year-old White Male
  Location: New York, NY

  Lab Results:
  - egfr 
  - 59 ml / min / 1.73
  - non african-american
  
  """
  
  swarm = MedicalCoderSwarm(patient_id="Patient-001", max_loops=1, patient_documentation="")
  
  swarm.run(task=patient_case)
  
  print(json.dumps(swarm.to_dict()))

Architecture Overview

flowchart TB
    CMO[Chief Medical Officer] --> V[Virologist]
    V --> I[Internist]
    I --> MC[Medical Coder]
    MC --> S[Synthesizer]
    

Features

• Specialized Agent Roles: Each agent has specific medical expertise and responsibilities
• Structured Diagnostic Flow: Organized pipeline from initial assessment to final synthesis
• ICD-10 Coding Integration: Comprehensive medical coding at each diagnostic stage
• Automated Report Generation: Standardized medical and coding reports
• Evidence-Based Decision Making: Multi-stage verification and synthesis process

Agent Responsibilities

mindmap
  root((Medical Swarm))
    Chief Medical Officer
      Initial Assessment
      Coordinate Specialists
      Treatment Plans
      Lab Range Analysis
    Virologist
      Viral Analysis
      Disease Progression
      Risk Assessment
    Internist
      System Review
      Vitals Analysis
      Comorbidity Evaluation
    Medical Coder
      ICD-10 Assignment
      Coding Compliance
      Documentation Review
    Synthesizer
      Integration
      Reconciliation
      Final Assessment

Diagnostic Flow Process

sequenceDiagram
    participant P as Patient Case
    participant CMO as Chief Medical Officer
    participant V as Virologist
    participant I as Internist
    participant MC as Medical Coder
    participant S as Synthesizer
    
    P->>CMO: Initial Data
    CMO->>V: Preliminary Assessment
    V->>I: Viral Analysis
    I->>MC: Comprehensive Review
    MC->>S: Coded Diagnosis
    S->>P: Final Report

Docker Usage

To build and run the Docker container for the Medical Coder Swarm, follow these steps:

1. Build the Docker Image:

docker build -t mcs .

2. Run the Docker Container:

docker run --rm mcs

Docker Compose

docker-compose up

To stop the services, run:

docker-compose down

Lab Range Analysis

The system includes specialized functionality for analyzing lab results against diagnostic criteria:

• Automated range checking for common tests (e.g., eGFR)
• Diagnosis-specific range validation
• Multi-factor analysis for complex diagnoses

Contributing

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

License

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

Acknowledgments

• Built with the Swarms framework
• Utilizes GPT-4 for advanced medical reasoning
• ICD-10 coding standards compliance

Contact

For questions and support, please open an issue in the repository.