ArticleNPJ digital medicine2018
Machine-learned epidemiology: real-time detection of foodborne illness at scale.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Transforming Public Health Practice with Artificial Intelligence: A Framework-Driven Approach.Healthcare (Basel, Switzerland) · 2026Article
- Integrating Statistical and Machine-Learning Approaches forMicroorganisms · 2025Article
- Identifying Key Drivers of Foodborne Diseases in Zhejiang, China: A Machine Learning Approach.Foods (Basel, Switzerland) · 2025Article
- Big data analytics in food industry: a state-of-the-art literature review.NPJ science of food · 2025Review
- Foodborne Event Detection Based on Social Media Mining: A Systematic Review.Foods (Basel, Switzerland) · 2025Article
- Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements.JAMA network open · 2024Observational
- Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.Nature reviews. Microbiology · 2024Review
- Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.Veterinary research · 2024Article
- Innovations in public health surveillance: An overview of novel use of data and analytic methods.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- Epidemiological Data Mining for Assisting with Foodborne Outbreak Investigation.Foods (Basel, Switzerland) · 2023Article
- Lessons from COVID-19 for rescalable data collection.The Lancet. Infectious diseases · 2023Review
- Priorities for successful use of artificial intelligence by public health organizations: a literature review.BMC public health · 2022Review
- Disparate impacts on online information access during the Covid-19 pandemic.Nature communications · 2022Article
- An evaluation of Internet searches as a marker of trends in population mental health in the US.Scientific reports · 2022Article
- Population-scale dietary interests during the COVID-19 pandemic.Nature communications · 2022Article
- The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.Artificial intelligence review · 2022Article
- Active syndromic surveillance of COVID-19 in Israel.Scientific reports · 2021Article
- Effects of Pope Francis' Religious Authority and Media Coverage on Twitter User's Attitudes toward COVID-19 Vaccination.Vaccines · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.