Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Development of a knowledge base for diagnostic reasoning in cardiology
Computers and Biomedical Research
Time series prediction using belief network models
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Machine Learning
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Identifying Markov Blankets with Decision Tree Induction
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Regression Modeling Strategies
Regression Modeling Strategies
Journal of Biomedical Informatics
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
IEEE Transactions on Evolutionary Computation
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
Guest Editorial: Intelligent data analysis in biomedicine
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Classification of Otoneurological Cases According to Bayesian Probabilistic Models
Journal of Medical Systems
Using intelligence techniques to predict postoperative morbidity of endovascular aneurysm repair
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Journal of Medical Systems
Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks
International Journal of Healthcare Information Systems and Informatics
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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 Bayesian network (PBN) as a new type of prognostic model that builds on the Bayesian network methodology, and implements a dynamic, process-oriented view on prognosis. A PBN describes the mutual relationships between variables that come into play during subsequent stages of a care process and a clinical outcome. A dedicated procedure for inducing these networks from clinical data is presented. In this procedure, the network is composed of a collection of local supervised learning models that are recursively learned from the data. The procedure optimizes performance of the network's primary task, outcome prediction, and handles the fact that patients may drop out of the process in earlier stages. Furthermore, the article describes how PBNs can be applied to solve a number of information problems that are related to medical prognosis.