Robust parameter extraction for decision support using multimodal intensive care data

• Main Authors: Clifford, Gari D. (Contributor), Long, William J. (Contributor), Moody, George B. (Contributor), Szolovits, Peter (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
• Format: Article
• Language: English
• Published: The Royal Society, 2011-12-01T18:06:46Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Library of Medicine (U.S.)
National Institute of Biomedical Imaging and Bioengineering (U.S.)
National Institutes of Health (NIH) (grant no. R01 EB001659)
National Center for Research Resources (U.S.) (grant no. U01EB008577)
Philips Medical Systems
Information and Communication University (ICU), Korea