Inspiration

We wanted to try out this challenge by using some fancy ML algorithms in NLP, but the structure of the problem was quite tricky and caught our attention.

What it does

Showcases a dashboard where a user bring in data, pick one of the provided algorithms and a threshold to calculate the results. We make use of Levenshtein distance to compare the data as well as weighting some inputs accordingly.

How we built it

We use the distance calculated through Levenshtein algorithm in addition to applying different weights to certain entries (i.e. gender). We believe this weight optimization can be more efficiently performed using more complicated ML algorithms.

Challenges we ran into

Mostly understanding what accuracy meant, as well as navigating the data

Accomplishments that we're proud of

Achieving a high accuracy

What we learned

how to use the levenshtein distance

What's next for Fuzzy patients

Hospitals can use this to see if patients were inputted multiple times.

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