A Continuous Time Bayesian Network Model For Cardiogenic Heart Failure

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1 Listen to Your Face: Inferring Facial Action Units from

host level network intrusion detection [21], dynamic system re-liability modeling [22], social network dynamics learning [23], cardiogenic heart failure diagnosis and prediction [24], and gene network reconstruction [25]. Dynamic Bayesian networks are widely used dynamic models for modeling the dynamic relationships among random variables,

Journal of Biomedical Informatics

ing social networks [24] and cardiogenic heart failure [25]. A continuous time Bayesian network (CTBN) is a graphical mod-el whose nodes are associated with random variables and whose state evolves continuously over time. As a consequence the evolu-tion of each variable depends on the state of its parents in the graph.

Assessing different deployment plans of a colorectal

model 2. Auður Friðriksdóttir, Leonid Churilov, Helen Dewey: Modelling acute stroke thrombolysis pathways with Value-Focused Process Engineering 3. Elena Gatti, Davide Luciani, Fabio Stella: A Continuous Time Bayesian Network model for cardiogenic heart failure 4. Martin Pitt: Modelling the impact of extending the onset to treatment time of

A continuous time Bayesian network model for cardiogenic

A continuous time Bayesian network model for cardiogenic heart failure E. Gatti D. Luciani F. Stella Published online: 8 December 2011 Springer Science+Business Media, LLC 2011 Abstract Continuous time Bayesian networks are used to diagnose cardiogenic heart failure and to anticipate its likely evolution. The proposed model overcomes

Event-Driven Continuous Time Bayesian Networks

time stamps of medication, exercise, and meals would indi-cate events that could be relevant for a patient s health out-comes. To capture the influence of events on state variables, we introduce a new model event-driven continuous time Bayesian networks (ECTBNs) where, in addition to state

1 A Functional Model for Structure Learning and Parameter

A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, and Carlos A. Jaramillo

Journal of Applied Logic

Continuous time Bayesian network. Performance function. Synergy. Multi-objective. evolves, optimization. The continuous time Bayesian network (CTBN) is a probabilistic graphical model that enables reasoning about complex, interdependent, and continuous-time subsystems. The model uses nodes to denote subsystems and arcs to denote conditional

MACHINE LEARNING COMPARED TO CONVENTIONAL STATISTICAL MODELS

heart failure, mechanical cardiac complications, cardiogenic shock or another MI resulting in death. 3 Therefore, accurate prognostic assessment may aid clinicians in predicting clinical progression and adverse events that might be prevented with greater foreknowledge of their impending risks.

Sensitivity Analysis of Continuous Time Bayesian Network

Formally, a continuous time Bayesian network Cover X consists of two components. The first is an initial distribution denoted P0 X over X, which can be specified as a Bayesian network B. This distribution P0 X is used for determining the probability distributions for the initial states of the process. The second is a continuous-time

Factored performance functions and decision making in

Factored performance functions and decision making in continuous time Bayesian networks. Authors: Liessman Sturlaugson, Logan Perreault, and John W. Sheppard. NOTICE: this is the

Event-Driven Continuous Time Bayesian Networks: An

ous time processes. Additionally, continuous-time Bayesian networks (CTBNs) [Nodelman et al., 2002, 2003], represents joint trajectories of discrete variables, as opposed to models of event streams in continuous time. In this work we intro-duce a model that can be viewed as a novel combination of

Inference Complexity in Continuous Time Bayesian Networks

states. A continuous time Bayesian network is a tuple N= hB;Ci. The Bayesian network Bhas nodes corre-sponding to Xand is used only for determining P(X 0), the initial distribution of the process. Evidence at the initial time (t= 0) is incorporated by setting evidence in Band performing Bayesian network inference. The continuous-time transition

Feature selection for the accurate prediction of septic and

types of shock: septic and cardiogenic. Septic shock is the most common, and it is caused by infection. The second most frequent form of shock is the cardiogenic shock and it is a result of a heart failure. The multiscale nature of the ShockOmics database allows to search for attributes that are related to mortality induced by these two types

Learning Parameters of Hybrid Time Bayesian Networks

Example 1 Consider the clinical condition of heart failure (Gatti et al., 2012). Heart failure is said to be cardiogenic when the cardiac muscle is the organ from which the circulatory failure is triggered. The strength of the heart muscle is represented by its pump (PP). Cardiogenic heart failure may be caused by acute myocardial infarction (AMI).

Using Continuous-Time Bayesian Networks for Standards-Based

Let X be a set of continuous-time Markov processes, X 1;:::;X n, each of which can take on a discrete set of values. A continuous time Bayesian network, N, over X consists of two components. First, an initial distribution P0 X, which can be specified as a Bayesian network Bover X. Second, a continuous transition model, specified as a directed

Implication of Ventricular Assist Devices in Extracorporeal

continuous variables. Univariate and multivariate Cox proportional-hazards models were used to identify risk factors for mortality on the waiting list and after OHT. The selection for variables for the multivariate Cox proportional hazards models was conducted using the Bayesian model averaging (BMA) method [8,9].