MEDIPEDIA (AI Medical Dictionary)

A comprehensive medical dictionary application designed for hospital use, featuring AI-powered term categorization, user management, and extensive medical term database.

Features

• Medical Term Management:

  • Add, edit, delete medical terms
  • Detailed term view with multiple information sections
  • Search and filter functionality

• AI Integration:

  • Automatic term categorization using machine learning
  • Body system prediction
  • Model training interface

• Data Management:

  • Import terms from CSV, Excel, PDF, and text files
  • json database backend
  • Manage categories, body systems, and tags

• Online Integration:

  • Fetch definitions from Merriam-Webster medical dictionary
  • Web browser integration for viewing sources

Installation

Prerequisites

• Python 3.6+
• Required packages:

  pip install tkinter requests beautifulsoup4 pandas PyPDF2 scikit-learn joblib pillow
  

Running the Application

1. Clone the repository or download the source files
2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python medical_dictionary.py
   

Usage

Basic Operations

1. Search for medical terms using the search bar
2. Add new terms through the Dictionary menu
3. Import terms from external files
4. Use AI suggestions for term categorization

Admin Features

• Manage users, categories, body systems, and tags
• Train the AI model with existing terms
• View all terms in the dictionary

Database Schema

The application uses json with the following tables:

• categories - Term categories
• tags - Term tags
• dictionary - Medical terms and definitions
• term_tags - Many-to-many relationship between terms and tags

AI Model

The application uses a MultinomialNB classifier with TfidfVectorizer for term categorization. The model is trained on:

• Default medical knowledge (initial training)
• User-added terms (when manually trained)

Model files:

• medical_category_model.joblib - Trained classifier
• medical_tfidf_vectorizer.joblib - Vectorizer

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
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

Support

For issues or questions, please open an issue on the GitHub repository or Email : akrashnoor2580@gmail.com

### Additional Files You Should Create:

1. **requirements.txt**:

requests>=2.26.0 beautifulsoup4>=4.10.0 pandas>=1.3.0 PyPDF2>=1.27.0 scikit-learn>=0.24.0 joblib>=1.0.0 pillow>=8.3.0

2. **LICENSE** (MIT License example):
```text
MIT License

Copyright (c) [2025] [Akrash Noor Awan]

Permission is hereby granted...

Repository Structure Recommendation:

/hospital-medical-dictionary
│   README.md
│   medical_dictionary.py
│   requirements.txt
│   LICENSE
│   screenshot.png