Cancer treatment decisions should be based on all available evidence. But this evidence is
complex and varied: it includes not only the patient's symptoms and expert knowledge of the …

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 …

Data classification is one of the most common tasks in the field of statistical analysis and
countless methods have been developed for this purpose over time. A common approach is …

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 …

The Recursive Bayesian Net formalism was originally developed for modelling nested
causal relationships. In this paper we argue that the formalism can also be applied to …

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

In complex cancer cases, Bayesian networks can support clinical experts in finding the best
patient-specific therapeutic decisions. However, the development of decision networks …

The central role played by uncertainty in medical decision making explains why medicine
was amongst the first areas where applications based on Bayesian networks were …

To support building and maintaining knowledge-based systems for real-life application
domains, sophisticated knowledgeengineering methodologies are available. As more and …

Bayesian networks have been introduced in the 1980s. Research to explore the use of the
formalism in the context of medical decision making started in the 1990s. The formalism …