Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit

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.