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Philos Trans A Math Phys Eng Sci. 2009 Jan 28;367(1887):411-29. doi: 10.1098/rsta.2008.0157.

Robust parameter extraction for decision support using multimodal intensive care data.

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1
Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. gari@mit.edu

Abstract

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

PMID:
18936019
PMCID:
PMC2617714
DOI:
10.1098/rsta.2008.0157
[Indexed for MEDLINE]
Free PMC Article
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