A production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents. # 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) MedicalSwarm is a sophisticated medical diagnosis and coding platform that leverages a coordinated swarm of specialized AI agents to deliver comprehensive medical assessments, accurate ICD-10 coding, and detailed clinical documentation. The system employs a hierarchical approach with specialized agents working in concert to analyze patient data, generate diagnoses, and ensure coding compliance. ## Key Features ### Multi-Agent Architecture - Chief Medical Officer: Coordinates diagnosis workflow and synthesizes findings - Virologist: Specializes in viral disease analysis and progression - Internist: Provides comprehensive internal medicine evaluation - Medical Coder: Ensures accurate ICD-10 coding and compliance - Diagnostic Synthesizer: Creates final integrated assessments ### Enterprise Integration - RAG (Retrieval-Augmented Generation) API support - Comprehensive logging and telemetry - Scalable batch processing capabilities - Configurable output formats and storage ### Clinical Documentation - Automated ICD-10 code assignment - Hierarchical Condition Category (HCC) coding - Evidence-based diagnostic rationale - Detailed clinical progression timelines ----- ## Installation ```bash pip install mcs ``` ## Onboarding To get started you must first set some envs in your `.env` ```env WORKSPACE_DIR="agent_workspace" OPENAI_API_KEY="your_key" MASTER_KEY="328928402" # your master key for security ``` ## 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())) ``` ## Example with HIPPA Grade Security ```python import json from mcs.main import MedicalCoderSwarm if __name__ == "__main__": # Extended Example Patient Case patient_case = """ Patient Information: - Name: John Doe - Age: 45 - Gender: Male - Ethnicity: White - Location: New York, NY - BMI: 28.5 (Overweight) - Occupation: Office Worker Presenting Complaints: - Persistent fatigue for 3 months - Swelling in lower extremities - Difficulty concentrating (brain fog) - Increased frequency of urination Medical History: - Hypertension (diagnosed 5 years ago, poorly controlled) - Type 2 Diabetes Mellitus (diagnosed 2 years ago, HbA1c: 8.2%) - Family history of chronic kidney disease (mother) Current Medications: - Lisinopril 20 mg daily - Metformin 1000 mg twice daily - Atorvastatin 10 mg daily Lab Results: - eGFR: 59 ml/min/1.73m² (Non-African American) - Serum Creatinine: 1.5 mg/dL - BUN: 22 mg/dL - Potassium: 4.8 mmol/L - HbA1c: 8.2% - Urinalysis: Microalbuminuria detected (300 mg/g creatinine) Vital Signs: - Blood Pressure: 145/90 mmHg - Heart Rate: 78 bpm - Respiratory Rate: 16 bpm - Temperature: 98.6°F - Oxygen Saturation: 98% Differential Diagnoses to Explore: 1. Chronic Kidney Disease (CKD) Stage 3 2. Diabetic Nephropathy 3. Secondary Hypertension (due to CKD) 4. Fatigue related to poorly controlled diabetes Specialist Consultations Needed: - Nephrologist - Endocrinologist - Dietitian for diabetic and CKD management Initial Management Recommendations: - Optimize blood pressure control (<130/80 mmHg target for CKD) - Glycemic control improvement (target HbA1c <7%) - Lifestyle modifications: low-sodium, renal-friendly diet - Referral to nephrologist for further evaluation """ # Initialize the MedicalCoderSwarm with the detailed patient case swarm = MedicalCoderSwarm( patient_id="Patient-001", max_loops=1, # patient_documentation=patient_case, output_folder_path="reports", key_storage_path="example_key.key", ) # Run the swarm on the patient case output = swarm.run(task=patient_case) # Print the system's state after processing print(json.dumps(swarm.to_dict(), indent=4)) ``` ## Architecture Overview ```mermaid 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 ```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 ``` ## 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 ``` ## API Usage We have established an api in the `/api` folder. To run the api locally you must git clone, and then run: ```bash cd api chmod +x bootup.sh ./bootup.sh ``` ### API Testing When you launch your api you can run the tests to see if it works ;) ```bash cd api python3 test.py ``` ## Docker Usage To build and run the Docker container for the Medical Coder Swarm, follow these steps: 1. **Build the Docker Image**: ```bash docker build -t mcs . ``` 2. **Run the Docker Container**: ```bash docker run --rm mcs ``` ## 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 - ICD-10 coding standards compliance ## Contact For questions and support, please open an issue in the repository. Source: https://github.com/The-Swarm-Corporation/MedicalCoderSwarm