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

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Food poisoning can strike anywhere hygiene standards are lax, but researchers have developed a new app that uses machine learning to help minimize the number of people affected. One out of every six U.S. residents gets food poisoning each year, and when they do, many of them write about it on Twitter. That's where nEmesis comes in. 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. The researchers developed their app by analyzing almost 4 million tweets generated by people in the New York City metropolitan area in late 2012 and early 2013.


Restaurant Reviews as Foodborne Illness Indicators

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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.


The Roslin Institute (University of Edinburgh) - News

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Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. The study highlights the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.