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Management - časopis za teoriju i praksu menadžmenta
2011, vol. 16, iss. 59, pp. 55-63
article language: Serbian, English
document type: unclassified

Hidden Markov Models for analyzing medical time series in order to detect nosocomial pneumonia
aUniversity Jena (FSU), Department of Information Systems, Jena, Germany
bUniversity Hospital, Jena, Germany

e-mail: marek.opuszko@uni-jena.de, j.ruhland@wiwi.uni-jena.de

Abstract

Pneumonia - as an inflammatory illness of the lung - is a dangerous and often fatal disease. A special subclass, the ventilator associated pneumonia (VAP), is affecting up to one fifth of the patients at Intensive Care Units (ICU). Based on a two years dataset, collected at a large ICU, we investigate a new method for time series processing in order to develop an early warning system for developing pneumonia. The system focuses on the pre-onset phase of the disease to extrapolate the future's course. We utilized the functionality of Hidden Markov Models and the stacking paradigm to categorize and forecast given time series of a patient. Finally we demonstrate the benefits of our approach with a set of real patient data.

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