New Data-driven Insights into Adult and Pediatric Type-2 Diabetes

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dc.contributor.committeeMemberSabharwal, Ashutoshen_US
dc.contributor.committeeMemberAazhang, Behnamen_US
dc.contributor.committeeMemberSano, Akaneen_US
dc.creatorAbbasi, Mahsanen_US
dc.date.accessioned2024-01-24T21:49:16Zen_US
dc.date.available2024-01-24T21:49:16Zen_US
dc.date.created2022-05en_US
dc.date.issued2023-12-01en_US
dc.date.submittedMay 2022en_US
dc.date.updated2024-01-24T21:49:16Zen_US
dc.description.abstractThe currently recommended strategies for managing diabetes rely primarily on broad, population-level data and average treatment effects observed in clinical trials. There is a critical need for an approach to personalizing type 2 diabetes (T2D) intervention, thereby refining treatment efficacy. In this thesis, we utilize unsupervised clustering techniques to investigate the various factors contributing to the prevalence and progression of T2D in individuals with different lifestyles and characteristics. We demonstrated our approach on two datasets from two projects. In the first project, we developed a data-driven framework to find physical activity-related phenotypes in an underserved T2D population. Moreover, we examine the association between physical activity measures and participants’ diabetes progression in the exclusive subgroups. In the second case, we classified pediatric patients into five distinct diabetes subtypes using K-Prototypes cluster analysis. Additionally, our findings provide new insights on how the treatment strategies and risk stratifications can differ even at the time of diabetes diagnosis based on a more precise characterization of pediatric T2D. In conclusion, the proposed data-driven approaches could bring us one step closer to precision therapies and individualized recommendations to become a routine part of diabetes management.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAbbasi, Mahsan. "New Data-driven Insights into Adult and Pediatric Type-2 Diabetes." (2023). Master's thesis, Rice University. https://hdl.handle.net/1911/115398en_US
dc.identifier.urihttps://hdl.handle.net/1911/115398en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectType-2 Diabetesen_US
dc.subjectCluster Analysisen_US
dc.titleNew Data-driven Insights into Adult and Pediatric Type-2 Diabetesen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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