Medicine and Health Sciences Commons^™

Dabigatran And Warfarin For Stroke Prevention In Atrial Fibrillation: Use, Switching, And Clinical Effects Following New Market Entry In Real-World Patients, Julie C. Lauffenburger '03

Doctoral Dissertations

Patients with atrial fibrillation frequently benefit from anticoagulation to prevent stroke and systemic embolism. For decades, warfarin was the primary oral anticoagulant option despite its narrow therapeutic index requiring monitoring and drug-drug interactions. Dabigatran’s recent availability provides practical advantages including no monitoring and fewer interactions; however, it lacks a convenient reversal agent for bleeding events. Currently, it is unclear what factors have driven anticoagulant utilization since dabigatran’s introduction, and little real-world evidence on the agents’ comparative effectiveness and safety is available. The objectives were to describe dabigatran and warfarin’s utilization and switching patterns and assess their comparative effectiveness and safety. …

Stem Cell Biology And Strategies For Therapeutic Development In Degenerative Diseases And Cancer, Angel A. Alvarez '98

Doctoral Dissertations

Stem cell biology is an exciting field that will lead to significant advancements in science and medicine. We hypothesize that inducing the expression of stem cell genes, using the embryonic stem cell gene nanog, will reprogram cells and dedifferentiate human mesenchymal stem cells into pluripotent stem cells capable of neural differentiation. The aims of initial studies are as follows:

Aim 1: Demonstrate that forced expression of the embryonic stem cell gene nanog induces changes in human mesenchymal stem cells to an embryonic stem cell-like phenotype.

Aim 2: Demonstrate that induced expression of nanog up-regulates the expression of multiple embryonic stem …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …