# Medical Diagnosis Swarm Architecture
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
A production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents.
## Architecture Overview
```mermaid
flowchart TB
CMO[Chief Medical Officer] --> V[Virologist]
V --> I[Internist]
I --> MC[Medical Coder]
MC --> S[Synthesizer]
style CMO fill:#f9f,stroke:#333,stroke-width:2px
style V fill:#bbf,stroke:#333,stroke-width:2px
style I fill:#bbf,stroke:#333,stroke-width:2px
style MC fill:#bfb,stroke:#333,stroke-width:2px
style S fill:#fbb,stroke:#333,stroke-width:2px
```
## 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
```mermaid
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
```
## Installation
```bash
pip install swarms
```
## Usage
```python
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()))
```
## Diagnostic Flow Process
```mermaid
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
```
## Output Format
The system generates two main types of reports:
1. **Medical Diagnosis Report**: Clinical findings and recommendations
2. **Coding Report**: Structured ICD-10 codes and documentation
Example Report Structure:
```markdown
# Medical Diagnosis and Coding Report
Generated: [Timestamp]
## Clinical Summary
[Diagnosis Details]
## Coding Summary
### Primary Diagnosis Codes
[ICD-10 Codes]
### Secondary Diagnosis Codes
[Additional Codes]
## Recommendations
[Next Steps]
```
## 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](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.
Raw data
{
"_id": null,
"home_page": "https://github.com/The-Swarm-Corporation/MedicalCoderSwarm",
"name": "mcs",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.10",
"maintainer_email": null,
"keywords": "artificial intelligence, deep learning, optimizers, Prompt Engineering",
"author": "Kye Gomez",
"author_email": "kye@apac.ai",
"download_url": "https://files.pythonhosted.org/packages/b0/60/9bab1bbaa124ce2e0d77c38deb71c61c25ddf71310bd851f48ecfb341e85/mcs-0.0.2.tar.gz",
"platform": null,
"description": "# Medical Diagnosis Swarm Architecture\n\n\n[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)\n\nA production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents.\n\n## Architecture Overview\n\n```mermaid\nflowchart TB\n CMO[Chief Medical Officer] --> V[Virologist]\n V --> I[Internist]\n I --> MC[Medical Coder]\n MC --> S[Synthesizer]\n \n style CMO fill:#f9f,stroke:#333,stroke-width:2px\n style V fill:#bbf,stroke:#333,stroke-width:2px\n style I fill:#bbf,stroke:#333,stroke-width:2px\n style MC fill:#bfb,stroke:#333,stroke-width:2px\n style S fill:#fbb,stroke:#333,stroke-width:2px\n```\n\n## Features\n\n- **Specialized Agent Roles**: Each agent has specific medical expertise and responsibilities\n- **Structured Diagnostic Flow**: Organized pipeline from initial assessment to final synthesis\n- **ICD-10 Coding Integration**: Comprehensive medical coding at each diagnostic stage\n- **Automated Report Generation**: Standardized medical and coding reports\n- **Evidence-Based Decision Making**: Multi-stage verification and synthesis process\n\n## Agent Responsibilities\n\n```mermaid\nmindmap\n root((Medical Swarm))\n Chief Medical Officer\n Initial Assessment\n Coordinate Specialists\n Treatment Plans\n Lab Range Analysis\n Virologist\n Viral Analysis\n Disease Progression\n Risk Assessment\n Internist\n System Review\n Vitals Analysis\n Comorbidity Evaluation\n Medical Coder\n ICD-10 Assignment\n Coding Compliance\n Documentation Review\n Synthesizer\n Integration\n Reconciliation\n Final Assessment\n```\n\n## Installation\n\n```bash\npip install swarms\n```\n\n## Usage\n\n```python\n\nfrom mcs.main import MedicalCoderSwarm\nimport json\n\nif __name__ == \"__main__\":\n # Example patient case\n patient_case = \"\"\"\n Patient: 45-year-old White Male\n Location: New York, NY\n\n Lab Results:\n - egfr \n - 59 ml / min / 1.73\n - non african-american\n \n \"\"\"\n \n swarm = MedicalCoderSwarm(patient_id=\"Patient-001\", max_loops=1, patient_documentation=\"\")\n \n swarm.run(task=patient_case)\n \n print(json.dumps(swarm.to_dict()))\n```\n\n## Diagnostic Flow Process\n\n```mermaid\nsequenceDiagram\n participant P as Patient Case\n participant CMO as Chief Medical Officer\n participant V as Virologist\n participant I as Internist\n participant MC as Medical Coder\n participant S as Synthesizer\n \n P->>CMO: Initial Data\n CMO->>V: Preliminary Assessment\n V->>I: Viral Analysis\n I->>MC: Comprehensive Review\n MC->>S: Coded Diagnosis\n S->>P: Final Report\n```\n\n## Output Format\n\nThe system generates two main types of reports:\n\n1. **Medical Diagnosis Report**: Clinical findings and recommendations\n2. **Coding Report**: Structured ICD-10 codes and documentation\n\nExample Report Structure:\n```markdown\n# Medical Diagnosis and Coding Report\nGenerated: [Timestamp]\n\n## Clinical Summary\n[Diagnosis Details]\n\n## Coding Summary\n### Primary Diagnosis Codes\n[ICD-10 Codes]\n\n### Secondary Diagnosis Codes\n[Additional Codes]\n\n## Recommendations\n[Next Steps]\n```\n\n## Lab Range Analysis\n\nThe system includes specialized functionality for analyzing lab results against diagnostic criteria:\n\n- Automated range checking for common tests (e.g., eGFR)\n- Diagnosis-specific range validation\n- Multi-factor analysis for complex diagnoses\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Acknowledgments\n\n- Built with the Swarms framework\n- Utilizes GPT-4 for advanced medical reasoning\n- ICD-10 coding standards compliance\n\n## Contact\n\nFor questions and support, please open an issue in the repository.\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Paper - Pytorch",
"version": "0.0.2",
"project_urls": {
"Documentation": "https://github.com/The-Swarm-Corporation/MedicalCoderSwarm",
"Homepage": "https://github.com/The-Swarm-Corporation/MedicalCoderSwarm",
"Repository": "https://github.com/The-Swarm-Corporation/MedicalCoderSwarm"
},
"split_keywords": [
"artificial intelligence",
" deep learning",
" optimizers",
" prompt engineering"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "dea8d73bf576867bf590a6a1e3aced1b5a3d2fc68f1131677963525baabaf761",
"md5": "9ef0bfde5c8205d018c517e649557aab",
"sha256": "f155f120c424a824c4219f9a025c4a89e2eed6c71075d2e1a2231b0e0c20aefd"
},
"downloads": -1,
"filename": "mcs-0.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9ef0bfde5c8205d018c517e649557aab",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.10",
"size": 7618,
"upload_time": "2024-12-21T02:04:15",
"upload_time_iso_8601": "2024-12-21T02:04:15.353815Z",
"url": "https://files.pythonhosted.org/packages/de/a8/d73bf576867bf590a6a1e3aced1b5a3d2fc68f1131677963525baabaf761/mcs-0.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b0609bab1bbaa124ce2e0d77c38deb71c61c25ddf71310bd851f48ecfb341e85",
"md5": "e2a1f258cd6f640e5abad0b5c752abc0",
"sha256": "62a1f8a47d761c304899f0cbe59e0ac300bf94aba191b19ee23ac5d5e28ee618"
},
"downloads": -1,
"filename": "mcs-0.0.2.tar.gz",
"has_sig": false,
"md5_digest": "e2a1f258cd6f640e5abad0b5c752abc0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.10",
"size": 7249,
"upload_time": "2024-12-21T02:04:17",
"upload_time_iso_8601": "2024-12-21T02:04:17.630196Z",
"url": "https://files.pythonhosted.org/packages/b0/60/9bab1bbaa124ce2e0d77c38deb71c61c25ddf71310bd851f48ecfb341e85/mcs-0.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-21 02:04:17",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "The-Swarm-Corporation",
"github_project": "MedicalCoderSwarm",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "swarms",
"specs": []
},
{
"name": "loguru",
"specs": []
},
{
"name": "swarms-models",
"specs": []
}
],
"lcname": "mcs"
}
JSON
