101 Coma Room Management System PFE

Project overview

• An intelligent platform that monitors and manages patients in coma rooms in real time.
• It collects and analyzes vital data using AI algorithms to assist medical staff, improves response time and enhances patient safety through predictive alerts, and integrates with hospital databases for accurate data tracking.

Objectives and impact

• Aimed at revolutionizing intensive care management through smart technology and predictive monitoring.
• Provide real-time alerts and analytics to reduce response times and support clinical decisions.

Responsibilities

• Design and implement AI models (TensorFlow) to analyze vital signals and generate predictive alerts.
• Develop computer vision components (OpenCV) for any visual monitoring requirements and integrate with the data pipeline.
• Build backend APIs and services using FastAPI to collect, store and serve patient data securely.
• Develop a cross-platform UI (Flutter) for staff dashboards and mobile notifications; ensure seamless integration with hospital databases.

Required skills & technologies

• Python, TensorFlow, OpenCV for ML/CV development and signal analysis.
• FastAPI for backend service development and RESTful integration with hospital systems.
• Flutter for mobile/desktop dashboard development and real-time alert presentation.
• Knowledge of medical data privacy/security best practices and database integration.

Deliverables, duration & team

• Deliver a working prototype demonstrating real-time data ingestion, ML inference, alerting, and a staff dashboard.
• Duration (as stated in the original brief): 6-8 month.
• Number of trainees: 2.

How to apply

• Apply using the form: Application form
• In your application, include a brief CV, relevant project or coursework examples (ML/CV/healthcare) and a short motivation explaining your role preference within the two-trainee team.