A new computer model is proving significantly more accurate in identifying potentially unsafe restaurants when compared with existing methods of consumer complaints and routine inspections.
The model uses machine learning and de-identified and aggregated search and location data from logged-in Google users, according to research led by Google and Harvard T.H. Chan School of Public Health.
The findings show that the model can help to determine the real-time durations in food safety.
Dır Foodborne diseases are common, expensive and thousands of Americans every year in emergency services. This new technique, developed by Google, can help restaurants and local health departments find problems more quickly before more public health issues occur. Google He is a Global Health Professor at Harvard Chan School and Director of the Harvard Global Health Institute.
The study was published online in November at npj Digital Medicine.
Foodborne diseases are a permanent problem in the United States, and up-to-date methods for detecting current outbreaks by restaurants and local health departments are primarily based on consumer complaints or routine inspections. The authors suggest that these methods may be slow and laborious, often leading to delayed responses and further spread of the disease.
To address these shortcomings, Google researchers developed the machine learning model and worked with Harvard to test in Chicago and Las Vegas. The model first works by classifying search queries that can show food-borne diseases such as ı stomach cramps Model or ları diarrhea Model. The model then uses undefined and aggregated location history data from the smartphones of those who choose to save it. To determine which restaurants are visited by users searching for these terms.
Health departments in each city were provided with a list of restaurants identified as potential sources of foodborne diseases by the model. Although the health inspectors did not know whether they were asked to inspect the new model or traditional methods, they would send city health inspectors to these restaurants. During the study, the health units continued their normal examination procedures.
In Chicago, where the model was used between November 2016 and March 2017, the model caused 71 inspections. The study found that the ratio of insecure restaurants among those identified by the model was 52.1% compared with 39.1% among audits triggered by a complaint-based system. Researchers say Chicago has one of the nation’s most advanced monitoring programs and is currently using social media mining techniques, but that this new model is more precise in identifying restaurants with food security breaches.
In Las Vegas, the model was distributed between May and August 2016. Compared to routine inspections by the health department, there was a higher sensitivity rate in identifying insecure restaurants.
When the researchers compared the model with the routine inspections conducted by health departments in Las Vegas and Chicago, 52.3 percent of the total ratio between the two cities of the unsafe restaurants identified by the model was the general rate of detection of unsafe restaurants by routine inspections. 22.7 percent in two cities.
Interestingly, this study showed that 38 percent of all cases identified by this model were not the last person visited by the person seeking food-related disease, who was searching for keywords related to symptoms. The authors noted that this is important because previous studies have shown that people tend to blame the last restaurant they go to, and that they can then complain to the wrong restaurant. The authors may take 48 hours or longer to be clinically symptomatic after a person’s exposure to foodborne diseases.
The new model performed better in terms of sensitivity, scale and delay (time elapsed with people becoming ill and outbreak detected) from complaints-based audits and routine audits. Researchers noted that the model would be best utilized as an addition to existing methods used by health departments and restaurants, enabling better prioritization of controls and internal food safety assessments. More proactive and timely responses to events may mean better public health outcomes. In addition, the model may be valuable for small and medium sized restaurants that cannot meet security operation personnel to implement advanced food safety monitoring and data analysis techniques.
The funding for this study came in part from the US Centers for Disease Control and Prevention.
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