title: Not just data: a method for improving prediction with knowledge.
abstract: Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK. Electronic address: barbaros@eecs.qmul.ac.uk.
authors.forename: Barbaros
authors.initials: B
authors.lastname: Yet
authors.affiliation: Centre for Trauma Science, Queen Mary, University of London, UK.
authors.forename: Zane
authors.initials: Z
authors.lastname: Perkins
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK.
authors.forename: Norman
authors.initials: N
authors.lastname: Fenton
authors.affiliation: The Royal London Hospital, London, UK.
authors.forename: Nigel
authors.initials: N
authors.lastname: Tai
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK.
authors.forename: William
authors.initials: W
authors.lastname: Marsh
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001777:Blood Coagulation; D001778:Blood Coagulation Disorders / Q000628:therapy*; D016000:Cluster Analysis; D003657:Decision Making; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted; D004632:Emergency Medical Services / Q000458:organization & administration; D006801:Humans; D019300:Medical Errors / Q000517:prevention & control; D008490:Medical Informatics / Q000379:methods* / Q000639:trends; D018570:Risk Assessment; D012680:Sensitivity and Specificity
keywords: Bayesian networks; Knowledge engineering; Latent variables