Machine-learned epidemiology: real-time detection of foodborne illness at scale.

ABSTRACT: Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3

SUBMITTER: Sadilek A 

PROVIDER: S-EPMC6550174 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature