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Engineering

App helps health department monitor foodborne illnesses

An app developed by University computer scientists is helping the Las Vegas health department improve the city's inspection protocols, Aaron Dubrow writes in a National Science Foundation press release.

The app, called nEmesis, uses natural language processing and artificial intelligence to identify food poisoning-related tweets, connects the tweets to restaurants using geotagging and identifies likely hot spots. It was developed by Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Professor of Computer Science, and Adam Sadilek, a former PhD student here now working at Google Research. Sean Brennan, a graduate student, and Vincent Silenzio, Associate Professor of Psychiatry, were also part of the team that worked on nEmesis.

During a recent collaboration with the Las Vegas health department, half of the city's restaurant inspections were performed using the traditional random approach and half were done using nEmesis, without the inspectors knowing that any change had occurred in the system.

Analyzing the results of the experiment, the researchers found the tweet-based system led to citations for health violations in 15 percent of inspections, compared to 9 percent using the random system. Some of the inspections led to warnings; others resulted in closures.

The researchers estimate that these improvements to the efficacy of the inspections led to 9,000 fewer food poisoning incidents and 557 fewer hospitalizations in Las Vegas during the course of the study.

"nEmesis has proved to be a useful tool for quickly and accurately identifying facilities in need of support, education, or regulation by the health department," says Lauren DiPrete, senior environmental health specialist for the Southern Nevada Health District.

Click here to read more about deploying nEmesis in Las Vegas.