To understand the types of false positive cases identified by an Influenza-like illness (ILI) text classifier by measuring the prevalence of ILI-related concepts that are negated, hypothetical, include explicit mention of temporality, experienced by someone other than the patient, or described in templated text that is difficult to process.
We describe here a multilingual ontology to support disease surveillance by intelligent text mining systems from Web-based rumours. We informally assessed the coverage of its English terms on large sample of news collected from the Web.
Presented April 25, 2018.
An estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.
Objective:
To incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.