Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge
This year's PhysioNet/Computers in Cardiology Challenge aimed to stimulate development of methods for identifying intensive care unit (ICU) patients at imminent risk of acute hypotensive episodes (AHEs), motivated by the possibility of improving care and survival of these patients. Participants...
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Acesso em linha: | http://hdl.handle.net/1721.1/60011 |
| _version_ | 1826205464493293568 |
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| author | Moody, George B. Lehman, Li-Wei H. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Moody, George B. Lehman, Li-Wei H. |
| author_sort | Moody, George B. |
| collection | MIT |
| description | This year's PhysioNet/Computers in Cardiology Challenge aimed to stimulate development of methods for identifying intensive care unit (ICU) patients at imminent risk of acute hypotensive episodes (AHEs), motivated by the possibility of improving care and survival of these patients. Participants were asked to forecast the occurrence of an AHE up to an hour in advance, in two groups of ICU patient records from the MIMIC II Database, drawing on data that included at least 10 hours of physiologic waveforms, time series, and accompanying clinical data prior to the one-hour forecast window. In event 1, most participants were able to identify without errors, in a group of 10 high-risk patients receiving pressor medication, which five of the patients experienced AHEs during the forecast window. In event 2, participants were able to classify correctly as many as 37 (93%) of a diverse group of 40 patients, including nearly all of those who experienced AHEs. |
| first_indexed | 2024-09-23T13:13:16Z |
| format | Article |
| id | mit-1721.1/60011 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:13:16Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/600112022-09-28T12:44:25Z Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge Moody, George B. Lehman, Li-Wei H. Harvard University--MIT Division of Health Sciences and Technology Moody, George B. Moody, George B. Lehman, Li-Wei H. This year's PhysioNet/Computers in Cardiology Challenge aimed to stimulate development of methods for identifying intensive care unit (ICU) patients at imminent risk of acute hypotensive episodes (AHEs), motivated by the possibility of improving care and survival of these patients. Participants were asked to forecast the occurrence of an AHE up to an hour in advance, in two groups of ICU patient records from the MIMIC II Database, drawing on data that included at least 10 hours of physiologic waveforms, time series, and accompanying clinical data prior to the one-hour forecast window. In event 1, most participants were able to identify without errors, in a group of 10 high-risk patients receiving pressor medication, which five of the patients experienced AHEs during the forecast window. In event 2, participants were able to classify correctly as many as 37 (93%) of a diverse group of 40 patients, including nearly all of those who experienced AHEs. National Institute of General Medical Sciences (U.S.) National Institutes of Health (U.S.) (Cooperative agreement U01-EB-008577) PhysioNet Resource (grant 2R01 EB001659) 2010-11-17T21:16:38Z 2010-11-17T21:16:38Z 2010-04 2009-09 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-7281-9 0276-6547 INSPEC Accession Number: 11229465 http://hdl.handle.net/1721.1/60011 Moody, G.B., and L.H. Lehman. “Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge.” Computers in Cardiology, 2009. 2009. 541-544. © 2010 IEEE. en_US Computers in Cardiology 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 Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Moody, George B. Lehman, Li-Wei H. Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title | Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title_full | Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title_fullStr | Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title_full_unstemmed | Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title_short | Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge |
| title_sort | predicting acute hypotensive episodes the 10th annual physionet computers in cardiology challenge |
| url | http://hdl.handle.net/1721.1/60011 |
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