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The Algorithm Is Able To Flag Restaurants With Food Safety Issues In Near Real Time

The tech giant is working with Harvard University to develop an algorithm that analyzes Google searches to spot which restaurants might have food safety issues.

Researchers say it’s capable of flagging possible offenders in ‘near real time.’

They created a machine-learning based algorithm to identify unsafe restaurants, training it to look for specific search terms and location data.

The model is called FINDER, or Foodborne Illness Detector in Real Time.

First, it classifies searches that contain certain terms, like ‘stomach cramps’ or ‘diarrhea.’

Health inspectors weren’t told which ones had been identified by the algorithm and which had received consumer complaints.

Researchers tested the system in Chicago and Las Vegas between 2016 and 2017 and received positive results.

They found the overall rate across both cities of unsafe restaurants detected by the model was 52.3 percent.

By comparison, the overall rate of unsafe restaurants detected by routine inspections was 22.7 percent.

‘[We] demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods,’ according to the study.

FINDER was also more effective than customer complaints, which were discovered to be accurate only 38 percent of the time.

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Researchers believe this is because most people assume the cause of their food poisoning was the last place they ate at, causing them to file a complaint at the wrong restaurant.

They point to medical studies that have shown foodborne illnesses can take 48 hours or even longer to become symptomatic after someone has been exposed.

‘The new model outperformed complaint-based inspections and routine inspections in terms of precision, scale, and latency (the time that passed between people becoming sick and the outbreak being identified),’ Harvard’s T.H. Chan School of Public Health said in a statement.

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