title: Probabilistic causal models of multimorbidity concepts.
abstract: Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases can be complex due to the various interactions between diseases. A literature review reveals that there is a variety of definitions that describe different concepts with respect to multimorbidity, both for the cause of multimorbidity as well as the implications of multimorbidity. To be able to aid computerized decision support systems within patient care, e.g. electronic clinical guidelines that can be personalized given the patient's problems, these multimorbidity aspects need to be defined rigorously in a formal language. In this paper, we employ causal Bayesian networks to define and analyze a novel framework that can be used to model a spectrum of aspects related to multimorbidity. We conclude that this framework provides a solid basis for modeling interactions between multiple diseases.
authors.affiliation: Radboud University Nijmegen, The Netherlands.
authors.forename: Martijn
authors.initials: M
authors.lastname: Lappenschaar
authors.forename: Arjen
authors.initials: A
authors.lastname: Hommersom
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Lucas
mesh_terms: D001499:Bayes Theorem; D015897:Comorbidity; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D012307:Risk Factors