title: Understanding disease processes by partitioned dynamic Bayesian networks.
abstract: For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder.
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands. Electronic address: mbueno@cs.ru.nl.
authors.forename: Marcos L P
authors.initials: ML
authors.lastname: Bueno
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands; Faculty of Management, Science and Technology, Open University, The Netherlands. Electronic address: arjenh@cs.ru.nl.
authors.forename: Arjen
authors.initials: A
authors.lastname: Hommersom
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands; Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands. Electronic address: peterl@cs.ru.nl.
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Lucas
authors.affiliation: Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands. Electronic address: m.lappenschaar@science.ru.nl.
authors.forename: Martijn
authors.initials: M
authors.lastname: Lappenschaar
authors.affiliation: Department of Psychiatry, Radboud University Nijmegen Medical Center, The Netherlands. Electronic address: Joost.Janzing@radboudumc.nl.
authors.forename: Joost G E
authors.initials: JG
authors.lastname: Janzing
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003863:Depression; D006801:Humans; D011618:Psychotic Disorders; D012680:Sensitivity and Specificity
keywords: Dynamic Bayesian networks; Heuristic algorithm; Multivariate time series; Non-homogeneous stochastic processes; Probabilistic graphical models; Psychotic depression