A study led by researchers at the Mountain View company and Harvard’s T.H. Chan School of Public Health describes a machine learning model that leverages search and location data to identify “potentially unsafe” restaurants.
As the study’s authors explain, FINDER takes in anonymous and aggregated logs from users who opt to share their location data. It identifies search queries indicative of food poisoning (e.g., “how to relieve stomach pain”) and then looks up restaurants visited by the users who performed those searches.
Lastly, for each applicable restaurant, it calculates the proportion of folks who stopped by and later showed evidence of foodborne illnesses in their searches.
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