🏥 MedGuard – AI-Powered Medication Safety Platform

Welcome to our submission for the Hugging Face GenAI Agents & MCP Hackathon!

This project showcases a production-grade multi-agent system powered by LangGraph and the Model Context Protocol (MCP), designed to analyze medication safety, detect dangerous drug interactions, and provide clinical decision support.

🎬 Live Demo Video | 🔗 GitHub Repository | 🤖 Claude Desktop Integration

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🏆 Hackathon Tracks

Track	Target	Status
Track 1: Building MCP	$10,000	✅ 10 MCP Tools + 3 Resources
Track 2: Consumer Use	$10,000	✅ Claude Desktop Integration
Track 3: Agentic Use	$10,000	✅ Multi-Agent LangGraph System
Blaxel Choice Award	$2,500	✅ Full Blaxel Platform Integration

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🚨 Why This Matters: The Problem We're Solving

💀 The Reality of Medication Errors 7,000-9,000 Americans die annually from medication errors $42 billion spent annually on preventable adverse drug events 1.5 million patients harmed yearly by medication errors Polypharmacy (5+ medications) affects 40% of seniors Drug interactions cause 125,000+ deaths annually in the US

✅ How MedGuard Helps Real-time DDI detection from 25+ curated interactions Pharmacogenomic analysis for personalized dosing Beers Criteria screening for elderly patients Clinical guideline compliance (AHA/ACC/ADA) Cost optimization with generic alternatives

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🌐 Project Overview

MedGuard leverages 5 autonomous AI agents that collaborate to perform comprehensive medication safety analysis:

Agent	Role	Key Features
🔍 Drug Interaction Agent	Analyzes DDIs using knowledge graphs	CYP enzyme conflicts, PubMed enhancement, severity scoring
👤 Personalization Agent	Patient-specific adjustments	Renal/hepatic dosing, pharmacogenomics, Beers Criteria
📋 Guideline Agent	Clinical compliance checking	AHA/ACC, ADA, ESC guidelines with evidence levels
💰 Cost Agent	Formulary optimization	Generic substitutions, therapeutic alternatives
📝 Explanation Agent	Synthesis and communication	Prioritized recommendations, patient-friendly summaries

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🔄 LangGraph Orchestration Architecture

┌────────────────────────────────────────────────────────────────────────────┐
│                     MedGuard Multi-Agent Orchestrator                       │
│                                                                            │
│   START                                                                    │
│     │                                                                      │
│     ▼                                                                      │
│   ┌─────────────────────────────┐                                          │
│   │  Drug Interaction Agent     │◄── Entry point (always runs first)       │
│   │  • 25+ known DDIs           │                                          │
│   │  • CYP enzyme analysis      │                                          │
│   │  • ML severity prediction   │                                          │
│   └───────────┬─────────────────┘                                          │
│               │                                                            │
│               ▼                                                            │
│   ┌─────────────────────────────┐                                          │
│   │   CONDITIONAL ROUTER        │◄── Severity-based intelligent routing    │
│   │   Based on risk level       │                                          │
│   └───────────┬─────────────────┘                                          │
│               │                                                            │
│    ┌──────────┼──────────┐                                                 │
│    │          │          │                                                 │
│    ▼          ▼          ▼                                                 │
│ "critical" "parallel" "low_risk"                                           │
│    │          │          │                                                 │
│    ▼          │          │                                                 │
│ ┌────────┐    │          │                                                 │
│ │ Human  │    │          │                                                 │
│ │ Review │    │          │                                                 │
│ │ FLAG   │    │          │                                                 │
│ └───┬────┘    │          │                                                 │
│     │    ┌────┴────┐     │                                                 │
│     │    │Parallel │     │                                                 │
│     │    │Execution│     │                                                 │
│     │    └────┬────┘     │                                                 │
│     ▼         ▼          ▼                                                 │
│   ┌─────────────────────────────┐                                          │
│   │ Personalization Agent       │                                          │
│   │ Guideline Compliance Agent  │◄── Run in parallel for efficiency        │
│   │ Cost Optimization Agent     │                                          │
│   └───────────┬─────────────────┘                                          │
│               │                                                            │
│               ▼                                                            │
│   ┌─────────────────────────────┐                                          │
│   │    Explanation Agent        │◄── Final synthesis & prioritization      │
│   │    • Safety score (0-100)   │                                          │
│   │    • Prioritized actions    │                                          │
│   │    • Patient-friendly text  │                                          │
│   └───────────┬─────────────────┘                                          │
│               │                                                            │
│               ▼                                                            │
│             END                                                            │
└────────────────────────────────────────────────────────────────────────────┘

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🛠️ MCP Server Integration (10 Tools + 3 Resources)

MCP Tools

#	Tool	Description	Parameters
1	analyze_medication_safety	Full 5-agent pipeline	patient_id, query
2	check_drug_interactions	DDI detection via Neo4j	medications[], allergies[]
3	get_personalized_dosing	Patient-specific dosing	patient_id, medication, indication
4	check_guideline_compliance	Clinical guideline check	patient_id, condition
5	optimize_medication_costs	Generic alternatives	medications[], insurance_type
6	get_patient_profile	Demographics & history	patient_id
7	search_clinical_guidelines	BioBERT vector search	query, limit
8	explain_medication_decision	Patient-friendly text	analysis, reading_level
9	search_pubmed_literature	PubMed via MCP Search	query, study_types[]
10	search_fda_safety_alerts	FDA alerts via MCP Search	drug_name, years

MCP Resources

URI	Description
guidelines://clinical-practice	Clinical practice guidelines database
database://drug-interactions	Drug interaction knowledge graph
alerts://fda-safety	FDA safety communications

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🗂️ Data Sources & Medical Knowledge Bases

Our platform uses real, authoritative, evidence-based medical data sources:

1. 💊 DrugBank – Drug Interaction Database

• Purpose: Curated drug-drug interactions with mechanisms and management
• Coverage: 25+ high-risk interaction pairs (expandable to 3,000+)
• Data: Severity, mechanism, clinical effect, management strategy, evidence level
• Usage: Core DDI detection for all medication safety analysis

2. 🧬 Pharmacogenomics Database (PharmGKB)

• Purpose: Genetic variants affecting drug metabolism
• Enzymes Covered: CYP2D6, CYP2C9, CYP2C19, CYP3A4, CYP1A2
• Phenotypes: Poor metabolizer, intermediate, normal, ultrarapid
• Usage: Personalized dosing recommendations based on genetic markers

3. 👴 AGS Beers Criteria (2023)

• Purpose: Potentially inappropriate medications in older adults
• Coverage: 30+ medication classes to avoid in elderly
• Source: American Geriatrics Society
• Usage: Automatic flagging for patients ≥65 years

4. 📋 Clinical Practice Guidelines

Organization	Guidelines Included
AHA/ACC	Heart Failure, AFib, CAD, Hypertension
ADA	Type 2 Diabetes Standards of Care 2024
ESC	European cardiovascular guidelines
KDIGO	Chronic Kidney Disease 2024

5. 🔬 PubMed/MEDLINE (via MCP Search)

• Purpose: Literature search for clinical evidence
• API: MCP Search protocol integration
• Usage: Enhance recommendations with recent research citations

6. ⚠️ FDA Safety Communications

• Purpose: Drug safety alerts, recalls, black box warnings
• API: MCP Search protocol integration
• Usage: Real-time safety alert checking

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📦 Technology Stack

Layer	Technology	Purpose
MCP Server	Python mcp SDK	10 tools, 3 resources, stdio transport
Orchestration	LangGraph StateGraph	Conditional routing, parallel execution
LLM	Claude 4 Sonnet / GPT-4o / Gemini 2.0	Medical analysis, synthesis
Knowledge Graph	Neo4j	Drug interaction network
Vector Search	Qdrant + BioBERT	Semantic guideline search
API	FastAPI	REST endpoints, HIPAA audit logging
Frontend	Gradio	Interactive demo UI
Databases	PostgreSQL, Redis	Patient data, session management
Cloud	Blaxel Platform	Serverless deployment, observability

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🧩 Core Components

🔍 Drug Interaction Agent (drug_interaction_agent_enhanced.py)

• Role: Primary safety analysis entry point
• Capabilities:
  • Known interaction database lookup (DrugBank)
  • CYP enzyme metabolic conflict detection
  • ML-based novel interaction prediction
  • PubMed literature enhancement
  • Severity classification (minor → moderate → major → critical)

👤 Personalization Agent (personalization_agent.py)

• Role: Patient-specific safety adjustments
• Capabilities:
  • Renal dose adjustments (eGFR-based)
  • Hepatic impairment considerations
  • Pharmacogenomic analysis (CYP variants)
  • Beers Criteria screening (age ≥65)
  • Polypharmacy detection (5+/10+ meds)

📋 Guideline Compliance Agent (guideline_compliance_agent.py)

• Role: Evidence-based standard verification
• Capabilities:
  • Condition-specific therapy checks
  • Missing therapy identification
  • Guideline citation with evidence levels
  • Therapeutic class mapping

💰 Cost Optimization Agent (cost_optimization_agent.py)

• Role: Formulary and cost efficiency
• Capabilities:
  • Brand → generic substitution
  • Therapeutic class alternatives
  • Insurance formulary optimization
  • Annual savings calculation

📝 Explanation Agent (explanation_agent.py)

• Role: Clinical synthesis and communication
• Capabilities:
  • Safety score calculation (0-100)
  • Prioritized recommendation list
  • Executive summary for clinicians
  • Patient-friendly explanations (adjustable reading level)

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🧑‍⚕️ Demo Patients

ID	Patient	Age	Key Demonstration
P001	John Smith	67	Warfarin + Aspirin (major bleeding risk), CKD Stage 3, CYP2C9*3
P002	Maria Garcia	45	Sertraline + Tramadol (serotonin syndrome risk)
P003	Robert Chen	72	8 medications, hyperkalemia risk, HF + COPD + CKD
P004	Sarah Johnson	55	Simvastatin + Amlodipine (CYP3A4 interaction, myopathy risk)
P005	James Wilson	78	6 Beers Criteria violations, CYP2D6 poor metabolizer

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🚀 Deployment Options

Option 1: Hugging Face Spaces

# Visit: https://huggingface.co/spaces/BilalS96/MedGuard

Option 2: Claude Desktop Integration

{
  "mcpServers": {
    "healthcare-multi-agent-system": {
      "command": "/path/to/run_mcp_server.sh",
      "args": [],
      "env": {
        "POSTGRES_HOST": "localhost",
        "POSTGRES_PORT": "5432",
        "POSTGRES_DB": "healthcare_agents",
        "POSTGRES_USER": "postgres",
        "POSTGRES_PASSWORD": "your_password",
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your_password",
        "REDIS_HOST": "localhost",
        "REDIS_PORT": "6379",
        "QDRANT_URL": "http://localhost:6333",
        "GOOGLE_API_KEY": "",
        "ANTHROPIC_API_KEY": "",
        "OPENAI_API_KEY": ""
      }
    }
  }
}

Option 3: Blaxel Platform

cd my-agent && bl deploy
bl run agent healthcare-multi-agent-system --data '{"inputs": "Analyze patient P001"}'

Option 4: Docker Compose

docker-compose up -d
# API: http://localhost:8000
# UI: http://localhost:7860

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📊 Example Analysis Output

🏥 MedGuard Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Patient: John Smith (P001) | Age: 67 | Medications: 4

⚠️ SAFETY SCORE: 55/100 (MODERATE RISK)
⚠️ REQUIRES CLINICAL REVIEW

━━━ CRITICAL FINDINGS ━━━

🔴 MAJOR DRUG INTERACTION: Warfarin + Aspirin
   Mechanism: Additive antiplatelet/anticoagulant effects
   Effect: Significantly increased bleeding risk (GI, intracranial)
   Management: Monitor INR closely, add PPI, use 81mg aspirin only
   Evidence: Established (PMID: 27432982)

🟠 RENAL ADJUSTMENT NEEDED: Metformin
   eGFR: 58 mL/min (threshold: 30)
   Action: Monitor renal function; avoid if eGFR <30

🟠 PHARMACOGENOMIC ALERT: Warfarin + CYP2C9*3
   Phenotype: Intermediate metabolizer
   Action: May require 20-30% lower warfarin dose

━━━ RECOMMENDATIONS ━━━

1. [CRITICAL] Review warfarin + aspirin combination
2. [HIGH] Add PPI for gastroprotection
3. [MODERATE] Consider CYP2C9 genotype-guided dosing
4. [LOW] Generic substitution available: Save $285/month

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🧑‍💻 Authors

MedGuard Team – MCP 1st Birthday Hackathon Submission

• Built with ❤️ for patient safety
• Leveraging state-of-the-art AI agent orchestration
• Production-ready architecture for healthcare applications

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📝 License

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

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🙏 Acknowledgments

• Anthropic for Claude and the MCP protocol
• Hugging Face for hosting and the hackathon
• Blaxel for serverless AI infrastructure
• DrugBank, PharmGKB, AGS, AHA/ACC/ADA for medical knowledge
• The healthcare AI community for inspiration

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Built for the MCP 1st Birthday Hackathon
Making medication safety accessible through AI agents
🏥 💊 🤖 🔬 💚