Department of Biostatistics Seminar/Workshop Series

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Complex Markov models to predict changes in organ failure in ICU patients

Linda Peelen

Post-doc Researcher, Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands

Wednesday, September 23, 1:30-2:30pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

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 disease. With their explicit states and probabilistic state transitions, Markov models seem to fit this purpose well.

In this seminar I will describe how we developed a set of complex Markov models based on clinical data, that can be used to identify temporal patterns, predict outcomes, and test clinical hypotheses. The 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. In the seminar I will discuss the design choices and 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.

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Topic revision: 14 Sep 2009, EveAnderson
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