MediMap - AI-Powered Healthcare Navigator for Women

MediMap is an intelligent healthcare navigation system specifically designed to help women assess urgency, find appropriate specialists, and book clinic appointments. It uses an AI-driven, multi-agent architecture to streamline patient intake, triage, and referrals.

Features

• Conversational Intake: Collects symptoms naturally using a conversational AI agent.
• Intelligent Triage: Assesses the urgency of symptoms using a machine learning model to provide a medical urgency score (Low, Medium, High).
• Smart Referrals: Recommends the right type of specialist (e.g., Gynecologist, General Physician) based on the reported symptoms.
• Clinic Matching: Finds nearby clinics that match the required specialty, taking into account factors like female doctors, safety scores, and ratings.
• Appointment Booking: Simulates booking an appointment with a chosen doctor at a recommended clinic.
• Follow-up Scheduling: Automatically schedules follow-up checks after an appointment.

Architecture

The system is built with a decoupled frontend and backend architecture:

• Frontend: A modern React application (Vite + TypeScript) using Tailwind CSS and shadcn/ui components for a clean, responsive user interface.
• Backend: A robust FastAPI backend in Python, connected to an SQLite database (medimap.db) for storing clinic and appointment data.
• AI Agents: Custom Python agents handle specific domains:
  • intake_agent: Extracts symptoms and pain levels from natural language.
  • triage_agent: Uses an ML model to determine urgency and recommend specialists.
  • referral_agent: Queries the database to find the best matching clinics.
  • appointment_agent & followup_agent: Handle booking logic and post-visit care.

Technology Stack

Frontend

• React 18 (with Vite)
• TypeScript
• Tailwind CSS (Styling)
• shadcn/ui (Component Library)
• Lucide React (Icons)
• React Router (Navigation)
• React Hook Form & Zod (Form validation)

Backend

• Python 3.10+
• FastAPI (RESTful API framework)
• Uvicorn (ASGI Server)
• Scikit-learn / Pandas (Machine Learning for triage)
• SQLite (Database)

Running the Application Locally

Prerequisites

• Node.js (v18 or higher)
• npm or bun
• Python 3.10+

1. Start the Backend Server

First, install the Python dependencies and run the FastAPI server:

# Navigate to the project root
# Install dependencies
python -m pip install -r backend/requirements.txt

# Start the server (runs on http://localhost:8000)
python -m uvicorn backend.main:app --reload --port 8000

2. Start the Frontend Server

Open a new terminal window, install the Node dependencies, and start the Vite development server:

# Navigate to the project root
# Install dependencies
npm install

# Start the development server (runs on http://localhost:8080)
npm run dev

3. Access the Application

Open your browser and navigate to http://localhost:8080 (or the port specified by Vite in your terminal) to use MediMap.

Database and ML Model

• Database: The local database is initialized automatically. It uses seed data from backend/data/clinics.json.
• ML Model: The triage_agent uses a pre-trained model backend/model.pkl to make urgency predictions based on the dataset features listed in backend/model_columns.pkl.

Contributing

1. Fork the project.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

License

Distributed under the MIT License.