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MG symptom management app

Symptom tracking for Myasthenia Gravis patients: MG-ADL assessments, D3.js trend charts, PDF summaries, and emergency alerts to doctors.

Status
Coursework
Period
2026
Area
Full-stack apps
Code
Code public
Stack
  • Python
  • Django
  • D3.js
  • HTML
MG-ADL symptoms tracked
8
  1. Patient input + device signals
  2. Django app (role-based)
  3. Visit + event history
  4. D3.js charts
  5. PDF summaries
  6. Email alerts + doctor warnings
Fig. 2 Data flow, left to right (top to bottom on small screens).

Problem

Myasthenia Gravis patients and their doctors need a shared record of how symptoms change over time, and a fast path when something goes wrong.

Approach

  • Role-based access for doctors and patients, with visit and clinical-event history.
  • The MG-ADL 8-symptom assessment with trend charts.
  • Interactive D3.js symptom-progression charts.
  • Automated PDF summaries with a clinical volatility signal.
  • An emergency workflow that turns patient device signals into email alerts and doctor warnings with real-time timestamps.

Architecture

A Django app with doctor and patient roles, D3.js progression charts, PDF summary generation, and an email alert path for emergencies.

Results

An end-to-end app from assessment entry to doctor alert.