2026-09-19T00:00:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/331052026-06-06T01:04:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roger Mark.
Abdala, Omar T
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-06-19T17:40:59Z
2006-06-19T17:40:59Z
2005
2005
http://hdl.handle.net/1721.1/33105
62221697
Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 81-82).
Over the past 20 years, there has been a large ongoing effort in the Laboratory for Computational Physiology (LCP) to collect and annotate large databases of physiologic signals in order to facilitate an open initiative to develop algorithms to automate a variety of clinical tasks [1]. The success of this approach motivated the collection of a temporal database of signals typically collected in the intensive care unit (ICU) from the Beth Israel Deaconess Medical Center (BIDMC), known as the Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) database [11]. Although certain basic clinical information was recorded about the patients in this database, much of the information available from the ICU, such as nursing notes, were not recorded. Approximately 5 years ago, the LCP began an initiative to collect a massive temporal database consisting of almost all the information available in the BIDMC ICU [3] known as MIMIC II. This database not only consists of the beside monitor waveforms collected in the original MIMIC database, but also most of the sources of information available to the clinical staff, ranging from medication drip rates and fluid balances, to subjective evaluation scales and discharge and clinical progress notes detailing patient interventions, reactions, clinical visits and past medical history.
by Omar T. Abdala.
M.Eng.
S.B.
82 leaves
5195703 bytes
5199227 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
The Annotation Station : an open source technology for data visualization and annotation of large biomedical databases
Open source technology for data visualization and annotation of large biomedical databases
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<Abstract>Over the past 20 years, there has been a large ongoing effort in the Laboratory for Computational Physiology (LCP) to collect and annotate large databases of physiologic signals in order to facilitate an open initiative to develop algorithms to automate a variety of clinical tasks [1]. The success of this approach motivated the collection of a temporal database of signals typically collected in the intensive care unit (ICU) from the Beth Israel Deaconess Medical Center (BIDMC), known as the Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC) database [11]. Although certain basic clinical information was recorded about the patients in this database, much of the information available from the ICU, such as nursing notes, were not recorded. Approximately 5 years ago, the LCP began an initiative to collect a massive temporal database consisting of almost all the information available in the BIDMC ICU [3] known as MIMIC II. This database not only consists of the beside monitor waveforms collected in the original MIMIC database, but also most of the sources of information available to the clinical staff, ranging from medication drip rates and fluid balances, to subjective evaluation scales and discharge and clinical progress notes detailing patient interventions, reactions, clinical visits and past medical history.</Abstract>
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