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title TissueTech Ulcer RAG: Autonomous Bedside Telemetry & Clinical Intelligence Ecosystem
emoji 🏥
colorFrom cyan
colorTo purple
sdk gradio
pinned true
license mit
short_description Real-time clinical telemetry analysis ecosystem utilizing Llama 3.3 and Groq Cloud LPU framework.

🏥 TissueTech Ulcer RAG: Autonomous Bedside Telemetry & Clinical Intelligence Ecosystem

"Synthesizing Biomedical Telemetry, Synchronizing Multi-Parameter Risk Indexing, Automating Bedside Patient Care."

TissueTech Ulcer RAG is an enterprise-grade, highly decoupled Clinical Decision Support System (CDSS) built during the 72-Hour Research Hackathon. Designed for high-density healthcare environments, this cognitive ecosystem coordinates advanced LLMs via Groq LPU acceleration and local semantic knowledge retrieval (RAG) to process real-time multi-modal streaming data from an affordable hospital mattress matrix.

Python Gradio Groq Llama 3.3


🔗 Quick Access & Collaborative Profiles

🚀 Production Deployments (Hugging Face)

👥 The TissueTech Hackathon Crew


🏗️ Technical Architecture & Dataflow

The system implements a strict automated data mapping pipeline:

  • Input: Raw telemetry (Pressure, Temperature, Moisture).
  • Process: Multi-parameter Risk Index calculation ($RI$).
  • Output: Context-injected AI medical reports via RAG.

🔗 Live Interactive Production Deployments

The complete clinical intelligence network is deployed across two specialized, synchronized software environments hosted live on Hugging Face Spaces: (Muhammad Bilal)

  • 🧠 Module 1: Vortex Clinical Assistant: 🚀 Launch Space — Deep Neon Cyber-Luxe Core Reasoning Bot.
  • 📡 Module 2: Bedside Telemetry RAG Dashboard: 🚀 Launch Space — Real-Time Hardware Emulation Matrix.

🏗️ Technical Architecture & Computational Dataflow

Unlike generic, consumer-grade large language wrappers constrained by static training data, this system implements a strict automated data mapping pipeline. The local inference loop intercepts incoming raw telemetry, structures it, and matches it with peer-reviewed biomedical texts.

               ┌────────────────────────────────────────┐
               │    Simulated Hardware Sensor Matrix    │
               │   (Pressure %, Temperature °C, Moist %)│
               └───────────────────┬────────────────────┘
                                   │
                                   ▼
               ┌────────────────────────────────────────┐
               │  Automated Multi-Parameter Risk Index  │
               │   RI = (0.50*P) + (0.30*T) + (0.20*M)  │
               └───────────────────┬────────────────────┘
                                   │
                                   ▼
               ┌────────────────────────────────────────┐
               │    Semantic Context Engine (RAG Hub)   │
               │ (Maps Alert Zones to Research Context) │
               └───────────────────┬────────────────────┘
                                   │
                                   ▼
        ┌──────────────────────────┴──────────────────────────┐
        ▼                                                     ▼
┌─────────────────────────────────────┐       ┌─────────────────────────────────────┐
│    🧠 VORTEX CLINICAL ASSISTANT     │       │     📡 BEDSIDE TELEMETRY RAG NODE   │
├─────────────────────────────────────┤       ├─────────────────────────────────────┤
│ • Task: Complex Clinical Reasoning  │       │ • Task: Real-Time Sensor Processing │
│ • Target: Pathophysiology Modeling  │       │ • Target: Tactical Nursing Alerts   │
│ • Context: Background & Literature  │       │ • Context: Hardware Array Layout    │
└──────────────────┬──────────────────┘       └──────────────────▲──────────────────┘
                   │                                             │
                   └─────────────── [Context Handshake] ─────────┘


TissueTech-Ulcer-RAG/
├── README.md
├── vortex-clinical-assistant/
│   ├── app.py
│   └── requirements.txt
└── tissuetech-bedside/
    ├── app.py
    └── requirements.txt

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