title: The use of predictive models to optimize risk of decisions.
abstract: The purpose of this paper is to set up a mathematical framework that risk assessors and regulators could use to quantify the "riskiness" of a particular recommendation (choice/decision). The mathematical theory introduced here can be used for decision support systems. We point out that efficient use of predictive models in decision making for food microbiology needs to consider three major points: (1) the uncertainty and variability of the used information based on which the decision is to be made; (2) the validity of the predictive models aiding the assessor; and (3) the cost generated by the difference between the a-priory choice and the a-posteriori outcome.
authors.affiliation: Gut Health and Food Safety, Institute of Food Research, Norwich Research Park, Colney Ln, Norwich NR4 7UA, United Kingdom. Electronic address: jozsef.baranyi@ifr.ac.uk.
authors.forename: József
authors.initials: J
authors.lastname: Baranyi
authors.affiliation: Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil.
authors.forename: Nathália
authors.initials: N
authors.lastname: Buss da Silva
mesh_terms: D001499:Bayes Theorem; D003657:Decision Making; D003661:Decision Support Techniques; D005516:Food Microbiology / Q000379:methods*; D005517:Foodborne Diseases / Q000382:microbiology / Q000517:prevention & control*; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods*; D035501:Uncertainty
keywords: Bayesian modelling; Decision analysis; Predictive microbiology; Risk assessment