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dc.contributor.advisorRoger Mark.en_US
dc.contributor.authorAbdala, Omar Ten_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2006-06-19T17:40:59Z
dc.date.available2006-06-19T17:40:59Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/33105
dc.descriptionThesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.en_US
dc.descriptionIncludes bibliographical references (leaves 81-82).en_US
dc.description.abstractOver 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.en_US
dc.description.statementofresponsibilityby Omar T. Abdala.en_US
dc.format.extent82 leavesen_US
dc.format.extent5195703 bytes
dc.format.extent5199227 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleThe Annotation Station : an open source technology for data visualization and annotation of large biomedical databasesen_US
dc.title.alternativeOpen source technology for data visualization and annotation of large biomedical databasesen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.and S.B.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc62221697en_US


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