Fighting food poisoning with machine learning 

Credit: Adam Sadilek, University of Rochester

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.

Location-based epidemiology is nothing new. John Snow, credited as the world’s first epidemiologist, used maps of London in 1854 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.

 

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