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How does Resbita predict the chances of getting food poisoning for every restaurant?

This post will explain how Resbita predicts the chances of getting food poisoning at any restaurant, where actual application may be found at Resbita.

  • How We Do It

By using AI (including machine learning and other techniques) and the great amount of information on the internet from sources such as social media, we developed a system to automatically detect venues that pose a public hazard.

  • Background

In 2013 the CDC (Center for Disease Control and Prevention) estimated that almost 17% of people in the US are effected by foodborne diseases every year. That's 47.8 million Americans, 128,000 of which were hospitilized, including 3,000 cases which resulted in death.

If that wasn't bad enough, those numbers aren't even comprehensive. There are many cases of food poisoning that go unreported, including symptoms such as nausea, vomiting, diarrhea, and others in which medical treatment was not sought out, or was misdiagnosed as acute gastroenteritis.

There is a lot of information on social networks and other websites about bad experiences at restaurants that poison their guests, but much of it is scattered - left in the public posts and other communications about bad experiences. Resbita compiles all of this data into one easy to use resource that reliably predicts where food poisoning is likely or unlikely to occur.

The way of building epidemic prevention warning system is to apply machine learning to big data and to develop a system that automatically detects venues likely to pose a public health hazard.

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