Prognostic models in medicine are usually been built using simple decision rules,
proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer …

Prognostic models are tools to predict the future outcome of disease and disease treatment,
one of the fundamental tasks in clinical medicine. This article presents the prognostic …

Physiological mechanisms in human biology, the progress of disease in individual patients,
hospital work-flow management: these are just a few of the many complicated processes …

We consider a Bayesian statistical approach to model-based prediction of a future patient's
response to a therapy, suitable in a wide range of clinical monitoring applications, especially …

OBJECTIVE: Bayesian networks are commonly used for presenting uncertainty and
covariate interactions in an easily interpretable way. Because of their efficient inference and …

Causal probabilistic networks, also called Bayesian networks, allow both qualitative
knowledge about the structure of a problem and quantitative knowledge, derived from case …

In intensive care medicine close monitoring of organ failure status is important for the
prognosis of patients and for choices regarding ICU management. Major challenges in …

Purpose Oncological treatment is being increasingly complex, and therefore, decision
making in multidisciplinary teams is becoming the key activity in the clinical pathways. The …

The improvement of medical care quality is a significant interest for the future years. The fight
against nosocomial infections (NI) in the intensive care units (ICU) is a good example. We …

A prognostic Bayesian network (PBN) is new type of prognostic model that implements a
dynamic, process-oriented view on prognosis. In a companion article, the rationale of the …