NOVEL STATISTICAL METHODS IN HEALTHCARE ANALYTICS FOR PEOPLE WITH DIABETES AND PREDIABETES

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dc.contributor.advisorSmith, Paulen_US
dc.contributor.advisorGao, Guodongen_US
dc.contributor.authorLiu, Shipingen_US
dc.contributor.departmentMathematical Statisticsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-02-04T06:30:21Z
dc.date.available2022-02-04T06:30:21Z
dc.date.issued2021en_US
dc.description.abstractA great amount of statistical tools and methods have been applied in health care analytics to assist decision making and improve the quality of diabetes related health care services.However, limitations of existing methods, new types of data, and specific demands in different areas are challenging current statistical tools. These challenges further encourage developing new statistical methods or extending existing methods to better fit different demands and improve performance of models and methods in practice. In this dissertation, we developed, applied, and extended many innovative statistical models and methods to address practical issues in health care of diabetes related population. Firstly, we developed a novel automated event detection method for univariate time series, Continuous Glucose Monitoring (CGM) data, from diabetic patients. Secondly, we invented a low-dimensional framework to classify and track longitudinal glucose status of CGM users based on within-subject analysis and unsupervised variable selection methods. Thirdly, we investigated the influence of daily activities on glucose series by applying a nonparamentric multivariate two sample test with independence assumption relaxed. Moreover, besides focusing on diabetic population, we also developed predictive models to access the risk of diabetes for population with prediabetes in later two chapters. Two types of response variables, binary indicator and HbA1c values, were used to aid different demands in practical healthcare services.en_US
dc.identifierhttps://doi.org/10.13016/9uae-nxke
dc.identifier.urihttp://hdl.handle.net/1903/28394
dc.language.isoenen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledCGMen_US
dc.subject.pquncontrolledDiabetesen_US
dc.subject.pquncontrolledevent detectionen_US
dc.subject.pquncontrolledPrediabetesen_US
dc.titleNOVEL STATISTICAL METHODS IN HEALTHCARE ANALYTICS FOR PEOPLE WITH DIABETES AND PREDIABETESen_US
dc.typeDissertationen_US

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