2026-09-20T07:03:45Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1197792026-06-06T00:48:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Amar Gupta.
Ferguson, Phillip (Phillip F.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-18T20:04:18Z
2018-12-18T20:04:18Z
2018
http://hdl.handle.net/1721.1/119779
1078783599
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis. "June 2018."
Includes bibliographical references (pages 61-72).
In this thesis, I analyzed multiple databases of eICU data and formulated methods of improving personnel response to the incidence of alerts in these environments. Multiple aspects of the eICU response workflow were considered such as the medications provided by the healthcare providers following the triggering of an alert, the settings selected by the personnel regarding the reactivation of the alerts, as well as specific responses and their correlations to specific alert types. A new algorithm for predicting the reactivation time of alerts has been developed, which takes into account the severity and type of alert, as well as the patient status.
by Phillip Ferguson.
M.Eng.
72 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Optimization of personnel response in an eICU environment
Thesis
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