Inspiration

*Recent Studies have shown that almost half of all Americans have trouble obtaining, understanding or acting on information that is important to their health *Patients with limited health literacy tend to be in poorer health, partake less frequently of preventative health measures and screenings, and become hospitalized more frequently, resulting in an estimated annual cost of $50-$73 billion *Elderly patients with limited health literacy are almost twice as likely to die

What it does

Converts audio to text. Then simplifies the complex medical terminology to layman's terms in order to give the patient a better understanding of the physicians dialogue.

How we built it

We built in stages where: Stage 1 = converting audio to text using IBM-Watson Stage 2 = Simplifying the medical jargon using Python code that called on a dictionary list of medical terminology and their synonyms Stage 3 = Further simplification of the phrases using NLP techniques

Challenges we ran into

Finding a library with a large list of medical terminology and their synonyms Implementing natural language processing (NLP) analysis

Accomplishments that we're proud of

Interfacing multiple programming languages (Ruby-on-Rails and Python) without a substantial problem

What we learned

How to use Ibm-Watson guide, NLP Techniques, What API's are and how to use them

What's next for Simple Talk

Next, we will work on having Simple Talk perform translations between foreign speaking languages

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