| dc.contributor.advisor | Una-May O'Reilly. | en_US |
| dc.contributor.author | Jaffe, Alexander Scott | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2018-01-12T20:59:38Z | |
| dc.date.available | 2018-01-12T20:59:38Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/113146 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 37-39). | en_US |
| dc.description.abstract | An acute hypotensive episode (AHE) is a life-threatening condition durich which a patient's mean arterial blood pressure drops below 60 mmHG for a period of 30 minutes. This thesis presents the development and evaluation of a series of Long short-term memory recurrent neural network (LSTM RNN) models which predict whether a patient will experience an AHE or not based on a time series of mean arterial blood pressure (ABP). A 2-layer, 128-hidden unit LSTM RNN trained with rmsprop and dropout regularization achieves sensitivity of 78% and specificity of 98%. | en_US |
| dc.description.statementofresponsibility | by Alexander Scott Jaffe. | en_US |
| dc.format.extent | 39 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | 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. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Long short-term memory recurrent neural networks for classification of acute hypotensive episodes | en_US |
| dc.title.alternative | LSTM RNN for classification of AHE | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 1018306404 | en_US |
