Secure AI-Powered Health Monitoring Platform on GCP

Author: Regis Nde Tene (Chopinregis)

Status: UNVERIFIED (Score: 0/100)

Duration: 1 Month Capstone

Executive Summary

This project involves developing a HIPAA-compliant, event-driven platform on Google Cloud Platform to ingest, process, and analyze diverse medical data (images, time-series signals). Utilizing Python for scientific computing and advanced algorithms, the system generates real-time insights, visualized through a React.js frontend, while incorporating robust database management and graph theory for network analysis to ensure scalability and data security.

Key Skills

Project Execution Log

Stage 1: GCP Infrastructure & Secure Database Design

This stage successfully laid the secure and compliant groundwork for the AI-Powered Health Monitoring Platform by establishing a dedicated GCP environment. Key achievements include configuring a custom VPC, implementing strict firewall rules, and deploying a highly available, HIPAA-compliant Cloud SQL for PostgreSQL database, all secured with granular IAM policies. This robust infrastructure is now prepared to host the platform's sensitive medical data and services.

Deliverables

Stage 2: Event-Driven Data Ingestion & Advanced Preprocessing

Deliverables

Stage 3: Scientific Computing, Signal/Image Analysis & Graph Modeling

Deliverables

Stage 4: Backend API Development & System Scalability Design

Deliverables

Stage 5: React.js Frontend for Secure Data Visualization

This stage successfully delivered the user-facing component of the health monitoring platform. By developing a secure React.js frontend, we enabled authenticated users to visualize complex medical data, such as images and time-series signals, from the GCP backend. This involved implementing secure authentication, designing intuitive UI components for diverse data types, integrating with the API, and ensuring HIPAA-compliant data handling practices and deployment readiness.

Deliverables