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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| Format: | Article |
| Language: | en_US |
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The Royal Society
2011
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| Online Access: | http://hdl.handle.net/1721.1/67339 https://orcid.org/0000-0001-8411-6403 https://orcid.org/0000-0002-7749-1034 |
| _version_ | 1826213038303215616 |
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| author | Clifford, Gari D. Long, William J. Moody, George B. Szolovits, Peter |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Clifford, Gari D. Long, William J. Moody, George B. Szolovits, Peter |
| author_sort | Clifford, Gari D. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:42:22Z |
| format | Article |
| id | mit-1721.1/67339 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:42:22Z |
| publishDate | 2011 |
| publisher | The Royal Society |
| record_format | dspace |
| spelling | mit-1721.1/673392022-09-29T15:37:04Z Robust parameter extraction for decision support using multimodal intensive care data Clifford, Gari D. Long, William J. Moody, George B. Szolovits, Peter Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Clifford, Gari D. Clifford, Gari D. Long, William J. Moody, George B. Szolovits, Peter 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 2011-12-01T18:06:46Z 2011-12-01T18:06:46Z 2008-10 Article http://purl.org/eprint/type/JournalArticle 1364-503X 0962-8428 http://hdl.handle.net/1721.1/67339 Clifford, G.D et al. “Robust parameter extraction for decision support using multimodal intensive care data.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367 (2009): 411-429. Web. 1 Dec. 2011. © 2008 The Royal Society https://orcid.org/0000-0001-8411-6403 https://orcid.org/0000-0002-7749-1034 en_US http://dx.doi.org/10.1098/rsta.2008.0157 Philosophical Transactions of the Royal Society A Mathematical, Physical and Engineering Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf The Royal Society Royal Society Publishing |
| spellingShingle | Clifford, Gari D. Long, William J. Moody, George B. Szolovits, Peter Robust parameter extraction for decision support using multimodal intensive care data |
| title | Robust parameter extraction for decision support using multimodal intensive care data |
| title_full | Robust parameter extraction for decision support using multimodal intensive care data |
| title_fullStr | Robust parameter extraction for decision support using multimodal intensive care data |
| title_full_unstemmed | Robust parameter extraction for decision support using multimodal intensive care data |
| title_short | Robust parameter extraction for decision support using multimodal intensive care data |
| title_sort | robust parameter extraction for decision support using multimodal intensive care data |
| url | http://hdl.handle.net/1721.1/67339 https://orcid.org/0000-0001-8411-6403 https://orcid.org/0000-0002-7749-1034 |
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