Mining data from intensive care patients

Authors:
Jan Ramon;Daan Fierens;Fabián Güiza;Geert Meyfroidt;Hendrik Blockeel;Maurice Bruynooghe;Greet Van Den Berghe
Affiliations:
Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Intensive Care Medicine, University Hospital, Leuven, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Computer Science, Katholieke Universiteit Leuven, Belgium;Department of Intensive Care Medicine, University Hospital, Leuven, Belgium
Venue:
Advanced Engineering Informatics
Year:
2007

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Abstract

In this paper we describe the application of data mining methods for predicting the evolution of patients in an intensive care unit. We discuss the importance of such methods for health care and other application domains of engineering. We argue that this problem is an important but challenging one for the current state of the art data mining methods and explain what improvements on current methods would be useful. We present a promising study on a preliminary data set that demonstrates some of the possibilities in this area.