Food Processing


Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media

AI Magazine

CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.


Algorithm reads tweets to figure out which restaurants make you sick

AITopics Original Links

Developed by computer-science researchers from the University of Rochester, the software uses natural language processing and artificial intelligence to identify food poisoning-related tweets, connect them to restaurants using geotagging and identify likely hot spots. Specifically, Las Vegas began incorporating nEmesis results in its targeting of restaurants to inspect on any given day. The researchers then used those tweets to generate a list of the highest-priority restaurants for inspections. As a result, there were 9,000 fewer food-poisoning incidents and 557 fewer hospitalizations in Las Vegas during the course of the study, the researchers estimated.


Restaurant Reviews as Foodborne Illness Indicators

@machinelearnbot

Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments. Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments.