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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| 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. |
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| AbstractList | 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. 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. 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.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. |
| Author | Clifford, G.D Long, W.J Szolovits, P Moody, G.B |
| AuthorAffiliation | 1 Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology Cambridge, MA 02139, USA 2 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Cambridge, MA 02139, USA |
| AuthorAffiliation_xml | – name: 2 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Cambridge, MA 02139, USA – name: 1 Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology Cambridge, MA 02139, USA |
| Author_xml | – sequence: 1 givenname: G.D surname: Clifford fullname: Clifford, G.D email: gari@mit.edu organization: Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridge, MA 02139, USA – sequence: 2 givenname: W.J surname: Long fullname: Long, W.J organization: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridge, MA 02139, USA – sequence: 3 givenname: G.B surname: Moody fullname: Moody, G.B organization: Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridge, MA 02139, USA – sequence: 4 givenname: P surname: Szolovits fullname: Szolovits, P organization: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridge, MA 02139, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18936019$$D View this record in MEDLINE/PubMed |
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biology. Recent developments in... Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in... |
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| SubjectTerms | Algorithms Blood pressure Clinical Errors Computer Communication Networks Critical Care - methods Data Fusion Data Interpretation, Statistical Decision Support Decision Support Techniques Electrocardiography Health Services Needs and Demand Heart rate Humans Information retrieval noise Intensive Care Unit Intensive Care Units Interpolation Missing data Models, Statistical Monitoring, Physiologic - methods Noise Reproducibility of Results Signal noise Signal Processing, Computer-Assisted Signal Quality Waveforms |
| Title | Robust parameter extraction for decision support using multimodal intensive care data |
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