Medicine and Health Sciences Commons^™

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

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

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 …

Computer-Assisted Lesion Classification And Intervention Planning For Prostate Cancer, Ryan M. Alfano

Electronic Thesis and Dissertation Repository

Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for classifying prostate cancer (PCa); however, it suffers from two major limitations: (1) complex, multi-dimensional signals make interpretation challenging and (2) inter-observer variability of lesion classification between physicians. Critically needed are methods for augmenting the interpretability of mp-MRI to assist in lesion classification. To meet this need, we leveraged a patient cohort with post-surgery pathologist-annotated transverse histology images registered to pre-surgery in-vivo mp-MRI with a measured target registration error. We developed a radiomics-based machine learning model trained on annotations for PCa vs. non-PCa, and found that a 5-feature Naïve-Bayes …

The Intersection Of Industry, Occupation, And Job Tasks With Hotel Room Cleaner Musculoskeletal Disorder Injuries: A Methods Approach To The Analysis Of California Workers’ Compensation Data, Pamela Vossenas

Dissertations and Theses

Abstract

Background

Hotel room cleaners are a high-risk occupation for musculoskeletal disorder (MSD) injuries among U.S. hotel workers. The Bureau of Labor Statistics (BLS) publishes occupational injury rate data for Maids and Housekeeping Cleaners, the Standard Occupation Classification closest to the job of a hotel room cleaner, and for the hotel industry, yet the BLS does not cross reference injury rate data by occupation and industry. This lack of occupational injury surveillance data limits MSD injury prevention and intervention efforts targeting this high-risk occupation in the hotel industry. Workers’ compensation (WC) data is another source of administrative data with potential …

Imaging Based Prediction Of Pathology In Adult Diffuse Glioma With Applications To Therapy And Prognosis, Evan Gates

Dissertations & Theses (Open Access)

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known.

In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the …

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 …

Investigating Diffusion Tensor Imaging Correlates Of Cognitive Impairment In Idiopathic Normal Pressure Hydrocephalus And Alzheimer's Disease, Omar Hasan, Omar Hasan

Dissertations & Theses (Open Access)

Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus¹ (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption². Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological …

Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …

Enhancing Drug Overdose Mortality Surveillance Through Natural Language Processing And Machine Learning, Patrick J. Ward

Theses and Dissertations--Epidemiology and Biostatistics

Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized …

Disrupting The Perioperative Opioid Gateway: Identification Of Risk Factors For New Persistent Post-Surgical Opioid Use, Gia Marie Pittet

All ETDs from UAB

A large portion of the American Opioid Crisis is due to opioid naïve patients who become new persistent post-surgical opioid users, although the risk factors for the development of this addiction are not well studied. The objective of this study was to analyze multiple layers of pre-operative and procedural risk factors using an ecological perspective theoretical framework in adult patients undergoing invasive surgery. We performed a retrospective analysis of 13,970 opioid naïve adults in a mixed surgical cohort with data available at the University of California Los Angeles that was merged with narcotics data for the State of California (IRB#19-000625). …

Applications Of Longitudinal Machine Learning Methods In Multi-Study Alzheimer's Disease Datasets, Charles F. Murchison

All ETDs from UAB

Advances in statistical learning models for prediction have led to broader application across a variety of disciplines, granting generalizations and adaptations that were previ-ously intractable even with advanced computational techniques. Among these is the al-lowance of correlated data with inherent paneled structure such as longitudinal or clus-tered data; adjustments which have already begun to be applied to a variety of supervised and unsupervised machine learning methods which had previously focused on cross-sec-tional data. These modifications have seen rudimentary testing in a number of applied disciplines where correlated data is commonly observed, including medical and clinical research. One field in particular …