Skip to content

A real-time application that scrapes public health databases, employs NLP for content analysis, and alerts users about drug safety updates tailored to their preferences

License

Notifications You must be signed in to change notification settings

techthumb1/Drug-Safety-Monitoring-Application

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

30 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Drug-Safety-Monitoring-Application

Drug Safety Monitoring App

Welcome to the Drug Safety Monitoring App, a holistic solution designed to augment and streamline pharmacovigilance through the power of AI. By integrating deep learning, machine learning, and a seamless web interface, our tool aids professionals in the biopharmaceutical domain to predict and monitor drug safety based on user reviews.

[] App Screenshot

Table of Contents

  • Features
  • Directory Structure
  • Getting Started
  • Tech Stack
  • Roadmap
  • Contributing
  • License

Features

  • Review Input Interface:** Seamlessly enter and manage drug reviews.
  • Safety Prediction: Advanced deep learning models offer predictions regarding drug safety based on reviews.
  • Interactive Visualizations: Extract insights from rich, interactive charts and plots related to drug reviews.
  • Database Management: Integrated with PostgreSQL, ensuring reliable and efficient data handling.
  • Responsive UI: Engage with a user-friendly and adaptive interface.
  • Real-time Monitoring: Monitor drug safety in real-time based on user reviews.

Getting Started

Clone the repository:

git clone https://github.com/your_username/DrugSafetyMonitoringApp.git

Setup the backend:

cd DrugSafetyMonitoringApp/backend
pipenv install
pipenv shell
flask db upgrade

Setup the frontend:

cd DrugSafetyMonitoringApp/frontend
npm install
npm start

Tech Stack

  • Backend: Flask, PostgreSQL
  • Frontend: React, Material UI, Plotly.js, Axios, React Router, React Bootstrap, HTML, CSS, JavaScript
  • ML/AI: TensorFlow, Keras, Scikit-learn, SpaCy

Roadmap

  • Integrate React-based frontend for dynamic UI experience.
  • Develop RESTful APIs for enhanced communication between frontend and backend.
  • Incorporate detailed drug analytics based on biopharmaceutical parameters.
  • Add user authentication and role-based access control.
  • Implement CI/CD pipelines for automated testing and deployment.

Contributing

Open to contributions! Whether it's bug fixes, feature suggestions, or documentation improvements, every bit helps. Kindly refer to the CONTRIBUTING.md file for guidelines.

License

MIT License. Check the LICENSE file for detailed information.

About

A real-time application that scrapes public health databases, employs NLP for content analysis, and alerts users about drug safety updates tailored to their preferences

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published