nEmesis continuously collects tweets throughout Las Vegas and connects the tweets to food venues. These tweets are evaluated by the language model to determine which are self-reports of symptoms of foodborne illness. Courtesy of Adam Sadilek, University of Rochester
It’s happened to many of us. We eat at a restaurant with less than ideal hygiene and come down with a nasty case of food poisoning.
Foodborne illness afflicts 48 million people annually in the U.S. alone; 120,000 individuals are hospitalized annually, and 3,000 die from the illness. In fact, one out of every six Americans gets food poisoning each year. And many of these sufferers write about it on Twitter.
Computer science researchers from the University of Rochester have developed an app for health departments that 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 team presented the results of its research at the 30th Association for the Advancement of Artificial Intelligence (AAAI) conference in Phoenix, AZ, in February. The project was supported by grants from the National Science Foundation (NSF), the National Institutes of Health and the Intel Science and Technology Center for Pervasive Computing.
Location-based epidemiology is nothing new. John Snow, credited as the world’s first epidemiologist, used maps of London in 1666 to identify the source of the Cholera epidemic that was rampaging the city (a neighborhood well) and in the process discovered the connection between the disease and water sources.
However, as the researchers showed, it’s now possible to deduce the source of outbreaks using publicly available social media content and deep learning algorithms trained to recognize the linguistic traits associated with a disease — “I feel nauseous,” for instance.
Learn more: nEmesis Fights Foodborne Illnesses with Machine Learning
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