MediTweet

• 

  El Trabuco

• 

  Architecture

Inspiration

Saving Lives One Tweet at a Time

What it does

An intelligent twitter bot that replies to medicine requests with actionable information (i.e. previous tweets with offers and pharmacies that have the medicine)

How I built it

Text Processing:

1. text to lower
2. Remove punctuation marks and numbers
3. Replace medicine names for a keyword
4. Remove stop words
5. Apply Spanish stemmer to the words
6. Create a vector representation of the text: size 100

Use NLTK and SKLEARN for the Tweet Classifier

Challenges I ran into

1. Tweets with Images
2. Lack of Data
3. Integration challenges in GCP

Accomplishments that I'm proud of

This system replies in quasi-realtime to people that need medicine now!

What I learned

Building bots that respond in real-time is hard

What's next for MediTweet

• Make the system more scalable and robust
• Process images within tweets
• Improve Classifier

Credits

• Special Credits to the folks from BUSCAMED (github)
• Leo Urbina, for helping use set up caching on the edge.

Built With

• flask
• google-app-engine
• google-cloud-sql
• jinja
• nltk
• python
• redis