The Patient to Doctor ratio is very large in the real world. We developed this project to bridge the gap between Doctors and Patients through Twitter.
What it does
The Doctor will be able to look at the top 100 popular tweets regarding Diseases and will be able to respond to them sitting at home at his goodwill. Also , the Doctor can visualize the analysis of the popularity of Diseases, choose the top tweets of the disease of his choice and respond.
How we built it
We extracted the top tweets using Twitter API keys and filtered the top tweets.The Doctor can then view these top tweets and respond accordingly. We made use of ELK stack for Data Visualization.
Challenges we ran into
Extracting the tweets from Twitter,Filtering and handling the tweets.
Accomplishments that we're proud of
What we learned
Effective use of Twitter API. Handling large amount of Data by splitting into default number of shards using Elastic search.
What's next for DocRespond
To make the responses and suggestions of all the Doctor's open to all the users.