Predicting the risk and trajectory of intensive care patients using survival models
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2007
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| Online Access: | http://hdl.handle.net/1721.1/38326 |
| _version_ | 1826195127641571328 |
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| author | Hug, Caleb W. (Caleb Wayne) |
| author2 | Peter Szolovits. |
| author_facet | Peter Szolovits. Hug, Caleb W. (Caleb Wayne) |
| author_sort | Hug, Caleb W. (Caleb Wayne) |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. |
| first_indexed | 2024-09-23T10:07:49Z |
| format | Thesis |
| id | mit-1721.1/38326 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:07:49Z |
| publishDate | 2007 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/383262019-04-10T23:10:22Z Predicting the risk and trajectory of intensive care patients using survival models Hug, Caleb W. (Caleb Wayne) Peter Szolovits. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (p. 119-126). Using artificial intelligence to assist physicians in patient care has received sustained interest over the past several decades. Recently, with automated systems at most bedsides, the amount of patient information collected continues to increase, providing specific impetus for intelligent systems that can interpret this information. In fact, the large set of sensors and test results, often measured repeatedly over long periods of time, make it challenging for caregivers to quickly utilize all of the data for optimal patient treatment. This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are constructed that predict individual patient survival probabilities at fixed intervals in the future. These models seek to help physicians interpret the large amount of data available in order to provide optimal patient care. We find that the survival predictions from our models are comparable to survival predictions using the SAPS score, but are available throughout the patient's ICU course instead of only at 24 hours after admission. Additionally, we demonstrate effective prediction of patient mortality over fixed windows in the future. by Caleb W. Hug. S.M. 2007-08-03T18:30:16Z 2007-08-03T18:30:16Z 2006 2006 Thesis http://hdl.handle.net/1721.1/38326 154318376 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 126 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Hug, Caleb W. (Caleb Wayne) Predicting the risk and trajectory of intensive care patients using survival models |
| title | Predicting the risk and trajectory of intensive care patients using survival models |
| title_full | Predicting the risk and trajectory of intensive care patients using survival models |
| title_fullStr | Predicting the risk and trajectory of intensive care patients using survival models |
| title_full_unstemmed | Predicting the risk and trajectory of intensive care patients using survival models |
| title_short | Predicting the risk and trajectory of intensive care patients using survival models |
| title_sort | predicting the risk and trajectory of intensive care patients using survival models |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/38326 |
| work_keys_str_mv | AT hugcalebwcalebwayne predictingtheriskandtrajectoryofintensivecarepatientsusingsurvivalmodels |