HealthHelper

The home page. It includes a brief description of what the app does and how to use it.
The page where users can upload text from medical articles.
An example of the app's output.
The "Contact Us" page.

Inspiration

META
We were inspired by the Meta challenge and sought to find a way to make medical journal articles more palatable for the general masses.
GOOGLE CLOUD
We were also inspired by how powerful the Google Cloud Healthcare Natural Language API was and wanted to make an app that could harness its capabilities.
GE
Additionally, we were inspired by the GE challenge. Researchers and students can use this app to get quick summaries of long articles--automating the process of reading and rereading dense medical journal articles. This app could save people hours of work.

What it does

Users paste in text from a medical article and our app returns helpful keywords that help to get to the point about what the text says. The app can identify different types of medicines, procedures, health problems, and whether the problem is current or in the patient's history. This app can provide a quick way for people to review what happens in these wordy and jargon-filled medical journal articles.

How we built it

We used Flask for the backend framework and HTML and CSS for the frontend. We used Google Cloud's Healthcare Natural Language API in order to analyze the inputted text.

Challenges we ran into

Some of us had never used Flask before, while some of us only had a little bit of experience with HTML and CSS, so we learned a lot about all of these tools. We had never used Google Cloud's Healthcare Natural Language API before, so it took a long time to read and apply the documentation. The most difficult part was getting all of the information out of the JSON results from the API and displaying it on the page.

Accomplishments that we're proud of

Making a working web app that successfully analyzes any text pasted in by the user and returns helpful keywords with context!

What we learned

How to make API requests with Google Cloud's Healthcare Natural Language API, how to build a Flask app, how to use HTML and CSS.

What's next for HealthHelper

There are some parts of the JSON info from the API response that we didn't get to use. It would be great if we could include all of that information too. Additionally, we could add more to the front end and make it fancier.