Project Overview

This project addresses the challenge of optimizing medical treatments for individuals with allergies. In traditional healthcare, doctors face the complex task of considering a patient’s unique medical history when prescribing treatments. Allergic reactions to medications can be unpredictable, posing risks to patient safety. This AI-driven solution aims to streamline this process by leveraging a comprehensive dataset of patient information and allergies. By identifying correlations between individual profiles and the chemical compositions of prescribed products, the system assists doctors in making more informed decisions, ultimately minimizing the potential for allergic reactions and enhancing the overall effectiveness of medical treatments. In essence, it seeks to provide a personalized and data-driven approach to healthcare, promoting patient well-being through a more nuanced understanding of individual health factors.

Our application will use AWS services. Our group collectively worked on this project using git and github. For our frontend, we used react. We created our backend through MySQL and node.js. We then connected the frontend and backend so we could use our intended features like the patient survey. The connection was also required so doctors could login, which could then connect their patient data to their account. We used keras, tensorflow, jupyter notebooks, S3 bucket, and python to create and run our allergy detecting AI model.