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Predictive Modeling Using Clinical Data And Compositional Microbiome Data, Li Zhang
Predictive Modeling Using Clinical Data And Compositional Microbiome Data, Li Zhang
All ETDs from UAB
This dissertation focuses on developing predictive modeling using data from two areas: clinical data and microbiome data. Compositional data in real life are mainly represented by relative proportions. With the advancement of next-generation sequencing (NGS) technology, researchers can now collect a large volume of metagenomic sequencing data, which is valuable for investigating associations between the microbiome and host diseases. Current methods for dealing with such data are either constrained by generalization or limited by application. In the first part, to address this, we propose Bayesian compositional generalized linear models for analyzing microbiome data (BCGLM). This model incorporates a structured regularized …
Understanding Disparities In Hypertension Treatment Cascade And Validating Blood Pressure Measurement Among Persons Living With Or At Risk For Hiv, Jessica Blair
All ETDs from UAB
In the United States, 44% of adult women >18 years have hypertension. Although non-Hispanic Black adults were more likely to be aware of and treated for hypertension compared to non-Hispanic White adults, Black individuals had the highest prevalence of uncontrolled hypertension. Persons living with HIV have a higher prevalence of hypertension, which is a major modifiable risk factor for cardiovascular disease. Similar to the geographic distribution of hypertension in the US, people living in the South have higher incidence of both hypertension and HIV diagnoses compared to other US regions. Furthermore, accurate diagnosis of hypertension and initiation of treatment is …
Psychometric Evaluation Of A Perceived Barriers To Medication Adherence Instrument And Association Between Perceived Barriers And Patient Characteristics, Lynn Andreae
All ETDs from UAB
Medication adherence is an important component of diabetes management, yet nonadherence is common and has substantial patient and economic costs. In order to achieve meaningful improvements in medication adherence, clinicians and researchers need good ways of both identifying those at risk of nonadherence and ascertaining the specific reasons driving nonadherence in those patients. Current options for self-reported medication adherence are limited in their vulnerability to response bias, which may lead to overestimation of adherence. A measure that assesses perceived barriers to medication adherence may be a useful alternative, as it may identify patients at risk of nonadherence while reducing socially …