Bayesian networks for cardiovascular monitoring
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/1721.1/37920 |
| _version_ | 1826190367300517888 |
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| author | Roberts, Jennifer M. (Jennifer Marie) |
| author2 | George Verghese. |
| author_facet | George Verghese. Roberts, Jennifer M. (Jennifer Marie) |
| author_sort | Roberts, Jennifer M. (Jennifer Marie) |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. |
| first_indexed | 2024-09-23T08:39:12Z |
| format | Thesis |
| id | mit-1721.1/37920 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:39:12Z |
| publishDate | 2007 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/379202019-04-09T18:59:44Z Bayesian networks for cardiovascular monitoring Roberts, Jennifer M. (Jennifer Marie) George Verghese. 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. 83-85). In the Intensive Care Unit, physicians have access to many types of information when treating patients. Physicians attempt to consider as much of the relevant information as possible, but the astronomically large amounts of data collected make it impossible to consider all available information within a reasonable amount of time. In this thesis, I explore Bayesian Networks as a way to integrate patient data into a probabilistic model. I present a small Bayesian Network model of the cardiovascular system and analyze the network's ability to estimate unknown patient parameters using available patient information. I test the network's estimation capabilities using both simulated and real patient data, and I discuss ways to exploit the network's ability to adapt to patient data and learn relationships between patient variables. by Jennifer Roberts. S.M. 2007-07-18T13:11:14Z 2007-07-18T13:11:14Z 2006 2006 Thesis http://hdl.handle.net/1721.1/37920 135330263 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 85 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Roberts, Jennifer M. (Jennifer Marie) Bayesian networks for cardiovascular monitoring |
| title | Bayesian networks for cardiovascular monitoring |
| title_full | Bayesian networks for cardiovascular monitoring |
| title_fullStr | Bayesian networks for cardiovascular monitoring |
| title_full_unstemmed | Bayesian networks for cardiovascular monitoring |
| title_short | Bayesian networks for cardiovascular monitoring |
| title_sort | bayesian networks for cardiovascular monitoring |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/37920 |
| work_keys_str_mv | AT robertsjennifermjennifermarie bayesiannetworksforcardiovascularmonitoring |