The news is currently filled with images of crowded ER rooms, long lines, and overwhelmed hospital staff. I wanted to find a way to reduce the number of patients coming into hospital to 1) reduce further exposure to COVID-19 and 2) decrease stress for medical staff.

What it does

Using a compiled list of symptoms and risk factors from the CDC and other medical resources, I ran R-sentiment analysis on patient-inputted text to output a COVID-19 likelihood score. This score can then be presented to a physician, who will determine whether or not the patient should come in for treatment. A higher score indicates a more severe case.

Challenges I ran into

Since I am not in the medical field, I was not sure how to analyze the score, as in what score determines a "severe" case versus a "mild" case. However, I was able to break down the score into a ratio of positive versus negative factors, which helps to understand the score breakdown more. An example of this would be if two people had a score of 2: one person has 16 positive factors/8 negative factors whereas another person may have 2 positive factors/1 negative factor.

Accomplishments that I'm proud of

This was my first time using R-sentiment analysis, and I am proud that I was able to use a very effective program to hopefully give back to healthcare during this stressful period. I am also proud to have participated in my very first Hackathon!

What I learned

I learned how to look at a problem, design an idea to find a solution, and execute this idea.

What's next for COVET

With more research coming out about COVID-19, more detailed symptoms and risk factors can be added to the "Medical Terms Dictionary" I have created for this project. In addition, the code can be cleaned up more to be more inclusive for patients (such as not having to hyphenate compound-word symptoms).



r, sentiment-analysis, tidyverse

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