Towards a general theory of action and time
Artificial Intelligence
A model for reasoning about persistence and causation
Computational Intelligence
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Fundamenta Informaticae - Special issue: intelligent information systems
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Temporal reasoning for decision support in medicine
Artificial Intelligence in Medicine
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
International Journal of Approximate Reasoning
Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks
International Journal of Healthcare Information Systems and Informatics
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems
Artificial Intelligence in Medicine
Decision support system for Warfarin therapy management using Bayesian networks
Decision Support Systems
Artificial Intelligence in Medicine
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Prognostic models in medicine are usually been built using simple decision rules, proportional hazards models, or Markov models. Dynamic Bayesian networks (DBNs) offer an approach that allows for the incorporation of the causal and temporal nature of medical domain knowledge as elicited from domain experts, thereby allowing for detailed prognostic predictions. The aim of this paper is to describe the considerations that must be taken into account when constructing a DBN for complex medical domains and to demonstrate their usefulness in practice. To this end, we focus on the construction of a DBN for prognosis of carcinoid patients, compare performance with that of a proportional hazards model, and describe predictions for three individual patients. We show that the DBN can make detailed predictions, about not only patient survival, but also other variables of interest, such as disease progression, the effect of treatment, and the development of complications. Strengths and limitations of our approach are discussed and compared with those offered by traditional methods.