ArticleEpidemiology and infection2015
Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections.
Article in Epidemiology and infection, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.Veterinary research · 2024Article
- Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.International journal of microbiology · 2024Review
- Prediction ofLife (Basel, Switzerland) · 2023Article
- A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.BMC medical informatics and decision making · 2021Article
- Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.PloS one · 2021Article
- Article
- Methods for generating hypotheses in human enteric illness outbreak investigations: a scoping review of the evidence.Epidemiology and infection · 2019Article
- Preliminary Incidence and Trends of Infections with Pathogens Transmitted Commonly Through Food - Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2015-2018.MMWR. Morbidity and mortality weekly report · 2019Article
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- Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.PloS one · 2018Article
- Foodborne Diseases Active Surveillance Network-2 Decades of Achievements, 1996-2015.Emerging infectious diseases · 2015Article
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5 authors.
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Abstract
To design effective food safety programmes we need to estimate how many sporadic foodborne illnesses are caused by specific food sources based on case-control studies. Logistic regression has substantive limitations for analysing structured questionnaire data with numerous exposures and missing values. We adapted random forest to analyse data of a case-control study of Salmonella enterica serotype Enteritidis illness for source attribution. For estimation of summary population attributable fractions (PAFs) of exposures grouped into transmission routes, we devised a counterfactual estimator to predict reductions in illness associated with removing grouped exposures. For the purpose of comparison, we fitted the data using logistic regression models with stepwise forward and backward variable selection. Our results show that the forward and backward variable selection of logistic regression models were not consistent for parameter estimation, with different significant exposures identified. By contrast, the random forest model produced estimated PAFs of grouped exposures consistent in rank order with results obtained from outbreak data, with egg-related exposures having the highest estimated PAF (22·1%, 95% confidence interval 8·5-31·8). Random forest might be structurally more coherent and efficient than logistic regression models for attributing Salmonella illnesses to sources involving many causal pathways.
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