| dc.contributor.advisor |
Roger T. Mark and Peter Szolovits. |
en_US |
| dc.contributor.author |
Shu, Jennifer (Jennifer J.) |
en_US |
| dc.contributor.other |
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. |
en_US |
| dc.date.accessioned |
2007-04-03T17:07:50Z |
|
| dc.date.available |
2007-04-03T17:07:50Z |
|
| dc.date.copyright |
2005 |
en_US |
| dc.date.issued |
2005 |
en_US |
| dc.identifier.uri |
http://hdl.handle.net/1721.1/37064 |
|
| dc.description |
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. |
en_US |
| dc.description |
Includes bibliographical references (p. 87-90). |
en_US |
| dc.description.abstract |
The Laboratory for Computational Physiology is collecting a large database of patient signals and clinical data from critically ill patients in hospital intensive care units (ICUs). The data will be used as a research resource to support the development of an advanced patient monitoring system for ICUs. Important pathophysiologic events in the patient data streams must be recognized and annotated by expert clinicians in order to create a "gold standard" database for training and evaluating automated monitoring systems. Annotating the database requires, among other things, analyzing and extracting important clinical information from textual patient data such as nursing admission and progress notes, and using the data to define and document important clinical events during the patient's ICU stay. Two major text-related annotation issues are addressed in this research. First, the documented clinical events must be described in a standardized vocabulary suitable for machine analysis. Second, an advanced monitoring system would need an automated way to extract meaning from the nursing notes, as part of its decision-making process. The thesis presents and evaluates methods to code significant clinical events into standardized terminology and to automatically extract significant information from free-text medical notes. |
en_US |
| dc.description.provenance |
Made available in DSpace on 2007-04-03T17:07:50Z (GMT). No. of bitstreams: 2
82523535.pdf: 4937500 bytes, checksum: c0a8e132716d004d697304c5d56964e0 (MD5)
82523535-MIT.pdf: 4937311 bytes, checksum: 42ca9ae0b08f7bb274278a9bab1f0362 (MD5)
Previous issue date: 2005 |
en |
| dc.description.statementofresponsibility |
by Jennifer Shu. |
en_US |
| dc.format.extent |
90 p. |
en_US |
| dc.language.iso |
eng |
en_US |
| dc.publisher |
Massachusetts Institute of Technology |
en_US |
| dc.rights |
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. |
en_US |
| dc.rights.uri |
http://dspace.mit.edu/handle/1721.1/7582 |
|
| dc.subject |
Electrical Engineering and Computer Science. |
en_US |
| dc.title |
Free text phrase encoding and information extraction from medical notes |
en_US |
| dc.type |
Thesis |
en_US |
| dc.description.degree |
M.Eng. |
en_US |
| dc.contributor.department |
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. |
en_US |
| dc.identifier.oclc |
82523535 |
en_US |
