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dc.contributor.advisorPeter Szolovits and Ozlem Uzuner.en_US
dc.contributor.authorLuo, Yuan, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-03-03T21:09:58Z
dc.date.available2016-03-03T21:09:58Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/101575
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 157-181).en_US
dc.description.abstractThis dissertation applies subgraph mining and factorization algorithms to clinical narrative text, ICU physiologic time series and computational genomics. These algorithms aims to build clinical models that improve both prediction accuracy and interpretability, by exploring relational information in different biomedical data modalities including clinical narratives, physiologic time series and exonic mutations. This dissertation focuses on three concrete applications: implicating neurodevelopmentally coregulated exon clusters in phenotypes of Autism Spectrum Disorder (ASD), predicting mortality risk of ICU patients based on their physiologic measurement time series, and identifying subtypes of lymphoma patients based on pathology report text. In each application, we automatically extract relational information into a graph representation and collect important subgraphs that are of interest. Depending on the degree of structure in the data format, heavier machinery of factorization models becomes necessary to reliably group important subgraphs. We demonstrate that these methods lead to not only improved performance but also better interpretability in each application.en_US
dc.description.statementofresponsibilityby Yuan Luo.en_US
dc.format.extent181 pagesen_US
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/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleTowards unified biomedical modeling with subgraph mining and factorization algorithmsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc940768861en_US


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