Computer Model Identifies Risky Restaurants in Near Real Time
By Insurance Journal, January 26, 2019
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Foodborne illnesses are a persistent problem in the U.S. and current methods by restaurants and local health departments for determining an outbreak rely primarily on consumer complaints or routine inspections. The authors contend that these methods can be slow and cumbersome, often resulting in delayed responses and further spread of disease.
To counter these shortcomings, Google researchers developed a machine-learned model and worked with Harvard to test it in Chicago and Las Vegas. The model works by first classifying search queries that can indicate foodborne illness, such as “stomach cramps” or “diarrhea.” The model then uses de-identified and aggregated location history data from the smartphones of people who have opted to save it, to determine which restaurants people searching those terms had recently visited.
