Causality is an important concept throughout the health sciences and is particularly vital for
informatics work such as finding adverse drug events or risk factors for disease using …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are
growing in popularity. Their versatility and modelling power is now employed across a …

OBJECTIVES: Bayesian networks (BNs) are rapidly becoming a leading technology in
applied Artificial Intelligence, with many applications in medicine. Both automated learning …

An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial
importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) …

Life-course epidemiology relies on specifying complex (causal) models that describe how
variables interplay over time. Traditionally, such models have been constructed by perusing …

The Bayesian network (BN) is a promising method for modeling cancer metastasis under
uncertainty. BN is graphically represented using bioinformatics variables and can be used to …

Abstract Background and Objective Cardiovascular diseases are the leading death cause in
Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the …

Аннотация Реализация экономических рисков приводит к возникновению
нежелательных событий, которые характеризуются возможностью нанесения …

Contemporary industrial environments are usually constrained or limited in order to fit a fast,
cheap and non-error prone production. Human-like system capabilities are not generally …