Inspiration

Our inspiration came from personal experiences of struggling to accurately describe symptoms and discomforts during medical appointments. This challenge motivated us to create a tool that bridges the communication gap, making it easier to convey how you feel to healthcare professionals.

What it does

Struggling to Describe How You Feel? Our Symptom Interpreter translates your words into accurate medical terms, helping you better express your symptoms and prepare for your doctor's visit. Enter your symptoms, discomforts, or pain, and we'll provide clear, medically-accurate terminology with definitions to support you in communicating your health concerns.

How we built it

We developed the frontend using React.js with HTML, CSS, and JavaScript for a seamless user experience. The backend was built using C++ to ensure fast, efficient processing and accurate term mapping.

Challenges we ran into

One of the biggest challenges we faced was effectively communicating between the frontend and backend systems. Integrating the API with a backend built in C++ required careful coordination, as it presented compatibility and data exchange difficulties with the React.js frontend. Establishing smooth and reliable data flow, ensuring consistent responses, and optimizing API calls while maintaining system performance proved complex.

Accomplishments that we're proud of

We successfully built a functioning website from scratch that translates everyday descriptions into medical terms, a potentially life-changing tool for users. We created a user-friendly interface with React.js, utilized C++ for backend speed and efficiency, and developed a process to accurately map user inputs to medical terminology.

What we learned

This project allowed us to gain significant experience in user-centered design using Figma, develop proficiency in frontend technologies (React.js with HTML, CSS, and JavaScript), and enhance our backend skills with C++. We deepened our understanding of building functional, impactful applications and translating complex data interactions into simple user experiences.

What's next for MedLex

Moving forward, we plan to expand our symptom database to provide more accurate translations for a wider range of medical concerns. We're exploring the integration of a machine learning model to enhance the NLP system's precision and adaptability. By incorporating advanced algorithms, we aim to refine user inputs further and offer even more tailored medical descriptions. Additionally, we hope to enhance multilingual support, recognizing cultural nuances in symptom expression to reach broader audiences.

Built With

• c++
• css
• html
• javascript
• react.js