Bayesian networks applied to therapy monitoring
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Learning Bayesian networks with local structure
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Approximating Probabilistic Inference in Bayesian Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Network Construction and Updating Techniques for the Diagnosis of Acute Abdominal Pain
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning dynamic bayesian network structures from data
Learning dynamic bayesian network structures from data
Discovery and inclusion of SOFA score episodes in mortality prediction
Journal of Biomedical Informatics
Modern Applied Statistics with S
Modern Applied Statistics with S
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
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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 analyzing the multitude of data pertaining to the functioning of the organ systems over time are to extract meaningful clinical patterns and to provide predictions for the future course of diseases. With their explicit states and probabilistic state transitions, Markov models seem to fit this purpose well. In complex domains such as intensive care a choice is often made between a simple model that is estimated from the data, or a more complex model in which the parameters are provided by domain experts. Our primary aim is to combine these approaches and develop a set of complex Markov models based on clinical data. In this paper we describe the design choices underlying the models, which enable them to identify temporal patterns, predict outcomes, and test clinical hypotheses. Our models are characterized by the choice of the dynamic hierarchical Bayesian network structure and the use of logistic regression equations in estimating the transition probabilities. We demonstrate the induction, inference, evaluation, and use of these models in practice in a case-study of patients with severe sepsis admitted to four Dutch ICUs.