Robust parameter extraction for decision support using multimodal intensive care data
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 associat...
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Published in | Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 367; no. 1887; pp. 411 - 429 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
The Royal Society
28.01.2009
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ark:/67375/V84-N6NMJDFD-7 ArticleID:rsta20080157 istex:B0E1A5699BE970F2DA37909B08B782F9AB7D46C2 href:411.pdf Theme Issue 'Signal processing in vital rhythms and signs' compiled by Pablo Laguna and Leif Sörnmo ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1364-503X 1471-2962 |
DOI: | 10.1098/rsta.2008.0157 |