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Leo A. Celi. Fabre, Maria I Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2017-12-20T17:25:02Z 2017-12-20T17:25:02Z 2017 2017 http://hdl.handle.net/1721.1/112844 1015239594 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 26). Severity of illness scores are useful in quality improvement for benchmarking care in the ICU and in research for risk-adjustment. [1] However, these scores lack generalizability beyond the patients on whom the scores were trained on. An international consortium, Global Open Source Severity of Illness Score (GOSSIS) Project, has put together various databases to create a definition of critical care severity independent of geographic and cultural data. [2] In order to combine the databases efficiently, 200 variables of interest were identified and extracted from the databases, when possible. Some variables showed different distributions throughout the databases that can be attributed to differences in medical practices and selection bias of hospitals. Thus, the predictive models that were trained in one database scored poorly when tested on a different database. Thus, a combination of the extracted variables from different databases with some necessary data manipulation should create a more generalizable severity of illness score. by Maria I. Fabre. M.Eng. 72 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. International comparison of critically ill patients Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d0d4a270-5e72-43f2-8732-394a84ab9b49"> <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>International comparison of critically ill patients</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Fabre, Maria I</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>Severity of illness scores are useful in quality improvement for benchmarking care in the ICU and in research for risk-adjustment. [1] However, these scores lack generalizability beyond the patients on whom the scores were trained on. An international consortium, Global Open Source Severity of Illness Score (GOSSIS) Project, has put together various databases to create a definition of critical care severity independent of geographic and cultural data. [2] In order to combine the databases efficiently, 200 variables of interest were identified and extracted from the databases, when possible. Some variables showed different distributions throughout the databases that can be attributed to differences in medical practices and selection bias of hospitals. Thus, the predictive models that were trained in one database scored poorly when tested on a different database. Thus, a combination of the extracted variables from different databases with some necessary data manipulation should create a more generalizable severity of illness score.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>