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Roger G. Mark. Kyaw, Tin Htet Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-03-12T17:53:48Z 2007-03-12T17:53:48Z 2005 2005 http://hdl.handle.net/1721.1/36788 79624229 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. Includes bibliographical references (p. 117-120). Formatting data and executing time-oriented queries on a massive, multi-parameter clinical information database poses significant computational challenges. The challenges encountered in converting high-resolution waveform and trend signals in the MIMIC II (Multi-parameter Intelligent Monitoring for Intensive Care II) database from an error-prone proprietary format to a stable open-source WFDB (Waveform Database) format is presented in the first half of this thesis. The design and implementation of a search engine that is capable of executing time-series queries on clinical information in the MIMIC II database such as lab results, medications, and nurse-verified values from bedside monitors is presented in the second half of this thesis. The search engine employs simple algorithms with little storage overhead to identify time periods in patient records that satisfy time series criteria based on thresholds and gradients of unevenly-sampled measurements. Results from queries executed on the search engine to detect physiological events of clinical interest were presented. Case studies on patient records returned as hits for queries were performed to review the strengths and limitations of the search engine. by Tin Htet Kyaw. M.Eng. 120 p. 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. Formatting and searching a massive, multi-parameter clinical information database Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9e4d27f6-f719-4607-a6cd-6330eb74f769"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Formatting and searching a massive, multi-parameter clinical information database</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2005</PublicationDate> <Authors> <Author> <DisplayName>Kyaw, Tin Htet</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Formatting data and executing time-oriented queries on a massive, multi-parameter clinical information database poses significant computational challenges. The challenges encountered in converting high-resolution waveform and trend signals in the MIMIC II (Multi-parameter Intelligent Monitoring for Intensive Care II) database from an error-prone proprietary format to a stable open-source WFDB (Waveform Database) format is presented in the first half of this thesis. The design and implementation of a search engine that is capable of executing time-series queries on clinical information in the MIMIC II database such as lab results, medications, and nurse-verified values from bedside monitors is presented in the second half of this thesis. The search engine employs simple algorithms with little storage overhead to identify time periods in patient records that satisfy time series criteria based on thresholds and gradients of unevenly-sampled measurements. Results from queries executed on the search engine to detect physiological events of clinical interest were presented. Case studies on patient records returned as hits for queries were performed to review the strengths and limitations of the search engine.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>