Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Probability elicitation for belief networks: issues to consider
The Knowledge Engineering Review
Sensitivity analysis: an aid for belief-network quantification
The Knowledge Engineering Review
Risk analysis of a patient monitoring system using Bayesian Network modeling
Journal of Biomedical Informatics
Inference in hybrid Bayesian networks using dynamic discretization
Statistics and Computing
Dynamic Bayesian networks as prognostic models for clinical patient management
Journal of Biomedical Informatics
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Improving the science of healthcare delivery and informatics using modeling approaches
Decision Support Systems
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Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy management. The DSS is developed in collaboration with a Swedish hospital group that manages Warfarin therapy for more than 3000 patients. The proposed model can assist the clinician in making dose-adjustment and follow-up interval decisions, investigating variation causes, and evaluating bleeding and thrombosis risks related to therapy. The model is built upon previous findings from medical literature, the knowledge of domain experts, and large dataset of patients.