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Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto
Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto
Theses and Dissertations
In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …
Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett
Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett
All Theses
Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …
High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki
Doctoral Dissertations
Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter
Computational and Data Sciences (MS) Theses
Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …
The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist
The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist
Honors Theses
Technology has played an immense role in the evolution of healthcare delivery for the United States and on an international scale. Today, perhaps no innovation offers more potential than artificial intelligence. Utilizing machine intelligence as opposed to human intelligence for the purposes of planning, offering solutions, and providing insights, AI has the ability to alter traditional dynamics between doctors, patients, and administrators; this reality is now producing both elation at artificial intelligence's medical promise and uncertainty regarding its capacity in current systems. Nevertheless, current trends reveal that interest in AI among healthcare stakeholders is continuously increasing, and with the current …