Medicine and Health Sciences Commons^™

Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy

Research Collection School Of Computing and Information Systems

Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or …

An Investigation On The Effect Of Conserved Hinge Histidine On Influenza Hemagglutinin(Ha2) Protein Conformation Using Md Simulations, Nada Tolba

Chemistry & Biochemistry Undergraduate Honors Theses

Hemagglutinin is a protein on the surface of Human Influenza Viruses.¹ It is composed of two glycopolypeptide domains, the HA1 and HA2 domains. Previous studies have found that across different strains of Influenza viruses, HIS435 residues remain conserved.⁴ In studies where mutations occurred in hinge-site histadine residues, the Influenza virus was inactive.⁴ These investigations indicated a significant role of HIS435 (hinge-site histadines) in virulence. Four systems were created using Molecular dynamics (MD) simulations. Each system was composed of an Isolated HA2 trimer solvated in a 150 mM NaCl rectangular water box at 310 K under isobaric and …

The Safe And Effective Clinical Deployment Of Artificial Intelligence Tools, Kelly Nealon

Dissertations & Theses (Open Access)

18 million new cancer cases are diagnosed each year. Roughly half of these patients will be treated with radiation therapy, a complex technique that requires an interdisciplinary team of clinical staff and expensive equipment to be delivered safely. Cancer centers in Low- and Middle-Income Countries (LMIC) have an especially difficult time meeting the demands of radiation therapy as the complexity of treatment techniques increase, with only 37% of patients in these regions having access to the care they need. Artificial Intelligence (AI) based tools are being developed to simplify the treatment planning and quality assurance processes to increase the number …

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid

Dissertations & Theses (Open Access)

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …

Optimal R&D Investment In The Management Of Invasive Species, William Haden Chomphosy, Dale T. Manning, Stephanie A. Shwiff, Stephan Weiler

United States Department of Agriculture Wildlife Services: Staff Publications

Invasive alien species (IAS) threaten world biodiversity, ecosystem services, and economic welfare. While existing literature has characterized the optimal control of an established IAS, it has not considered how research and development (R&D) into new removal methods or technologies can affect management decisions and costs over time. R&D can lower the costs of control in a management plan and creates an intertemporal trade-off between quick but costly control and gradual but cheaper removal over time. In this paper, we develop and solve a continuous time dynamic optimization model to study how investment in R&D influences the optimal control of an …

Automating The Radiation Therapy Treatment Planning Process For Pediatric Patients With Medulloblastoma, Soleil Hernandez

Dissertations & Theses (Open Access)

Over the past 50 years, pediatric cancer 5-year survival rates increased from 20% to 80% in high-income countries, however, these trends have not been mirrored in low-and-middle-income countries (LMICs). This is due in part to delayed diagnosis, higher rates of advanced disease at presentation and a growing lack of access to high quality medical personnel and technology necessary to deliver complex treatments.

The long-term goal of this study was to alleviate demanding workflows and increase global access to high-quality pediatric radiation therapy by harnessing the power of artificial intelligence to automate the radiation therapy treatment planning process for pediatric patients …

Data Science For Hospital Antibiotic Stewardship, Saikou Jawla

Theses and Dissertations

Antibiotics are widely used to treat bacterial infections, but their misuse leads to antibiotic resistance. Antibiotic resistance is one of the biggest threats to global health, food security, and development today. Antibiotic resistance leads to higher medical costs, prolonged hospital stays, and increased mortality. Antimicrobial stewardship is an approach to measure and improve the appropriate use of antibiotics in healthcare settings. Data science has the potential to support these programs by providing insights into antibiotic prescribing patterns, identifying areas for improvement, and predicting patient outcomes. We explored the role of data science in hospital antibiotic stewardship programs, including statistical methods …

Peripheral Blood Mononuclear Cell Mitochondrial Dysfunction In Acute Alcohol-Associated Hepatitis, Annette Bellar, Nicole Welch, Jaividhya Dasarathy, Amy Attaway, Ryan Musich, Avinash Kumar, Jinendiran Sekar, Saurabh Mishra, Yana I. Sandlers, Et. Al

Chemistry Faculty Publications

Background: Patients with acute alcohol-associated hepatitis (AH) have immune dysfunction. Mitochondrial function is critical for immune cell responses and regulates senescence. Clinical translational studies using complementary bioinformatics-experimental validation of mitochondrial responses were performed in peripheral blood mononuclear cells (PBMC) from patients with AH, healthy controls (HC), and heavy drinkers without evidence of liver disease (HD).
Methods: Feature extraction for differentially expressed genes (DEG) in mitochondrial components and telomere regulatory pathways from single-cell RNAseq (scRNAseq) and integrated 'pseudobulk' transcriptomics from PBMC from AH and HC (n = 4 each) were performed. After optimising isolation and processing protocols for functional studies in …

Design And Synthesis Of Peripherally Selective Endocannabinoid Enzyme Inhibitors For Ocular Indications, Kezia Reji Thomas

Senior Honors Theses

Peripherally selective compounds have been found to stimulate endocannabinoid receptor activity, which has been observed to have positive physiological effects such as ocular wound healing and inflammation control. The activation of the cannabinoid 1 receptor via binding of the endogenous ligands, anandamide and 2-arachidonoylglycerol, has been indicated to elicit these effects. Both ligands are controlled by two hydrolase enzymes, fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), which can be targeted for therapeutic inhibition. Sulfonamide derivatives of JZL195 containing carbamate functionalities in the southern region of the inhibitor compounds were produced using novel carbamate exchange reactions. Polar functionalities were …

Interdisciplinary And Innovative: A Nursing And Computer Science Collaboration To Create A Barcode Medication Administration System, Jodi Lisbeth Berndt, Peter Ohmann, Imad Rahal, Lindsey Hoeschen, Andrew Rothstein

Nursing Faculty Publications

Preventing medication errors remains a priority in nursing education. The implementation of Barcode Medication Administration (BCMA) systems is one strategy that has been used to reduce medication errors. Practice using BCMA in simulated settings may enhance the transfer of these skills to the clinical practice setting. However, the purchase of BCMA educational products available for nursing students can be cost prohibitive for many nursing programs. To overcome the barrier of cost, an interdisciplinary and innovative collaborative approach was used to create a fully functional low-cost BCMA system.

Development Of A Cost-Constrained Intelligent Prosthetic Knee With Real-Time Machine Learning, Predictive Stumble Control, Lucas Jonathan Galey

Open Access Theses & Dissertations

The field of biomechatronics is evolving quickly with advances in computer science, biology, and electrical and mechanical engineering. Coupled with increased interests in machine learning (ML) across all industry sectors, there are opportunities to leverage advanced analytics in uniquely complex problems. This study aimed to deploy real-time ML predictions in a novel microprocessor-controlled prosthetic knee (MPK) device capable of identifying and responding to stumble-events to reduce amputee fall prevalence. Innately, stumbling is a chaotic event. Current MPKs operate by detecting gait characteristics and reacting to preprogrammed states. While these systems are beneficial in significant ways, such as energy expenditure and …

Development Of Surface-Modified Liposomes For Drug Delivery Applications, Megan Louise Qualls

Doctoral Dissertations

Liposomes are spherical vesicles composed of a lipid bilayer membrane that assembles around an internal aqueous core. This duality gives liposomes the ability to encapsulate both hydrophobic cargo within the lipid bilayer and hydrophilic cargo in the aqueous core, making them versatile molecular carriers for drug delivery. Liposome platforms have many advantages and are promising drug delivery carriers, and research is ongoing to improve their designs for continued clinical applications. Many liposome types have been developed, but further work is needed to improve surface modification, site-specific targeting, and triggered cargo release in order to further the therapeutic applications of these …

Modeling Antihypertensive Therapeutic Inertia And Intensification To Support Clinical Action Toward Hypertension Control, Benjamin Martin

All Dissertations

Background

Hypertension is the leading modifiable risk factor for cardiovascular disease and consequent mortality worldwide. In the U.S., more than half of hypertension cases remain uncontrolled, despite availability of effective pharmaceutical treatment options. Evidence suggests that therapeutic inertia, defined as clinician failure to initiate or increase therapy when treatment goals are unmet, is the most influential barrier to improving hypertension control. Substantial rates of therapeutic inertia have been reported in ambulatory primary care settings where hypertension is typically treated and managed. Understanding and overcoming the forces driving therapeutic inertia in hypertension management is a critical strategy to reach population health …

Treatment Planning Automation For Rectal Cancer Radiotherapy, Kai Huang

Dissertations & Theses (Open Access)

Background

Rectal cancer is a common type of cancer. There is an acute health disparity across the globe where a significant population of the world lack adequate access to radiotherapy treatments which is a part of the standard of care for rectal cancers. Safe radiotherapy treatments require specialized planning expertise and are time-consuming and labor-intensive to produce.

Purpose:

To alleviate the health disparity and promote the safe and quality use of radiotherapy in treating rectal cancers, the entire treatment planning process needs to be automated. The purpose of this project is to develop automated solutions for the treatment planning process …

Outside, Looking In: A Dissertation On Mindful Walking And How Green Exercise Affects State Mindfulness And Connectedness To Nature, Dustin Wyatt Davis

UNLV Theses, Dissertations, Professional Papers, and Capstones

INTRODUCTION: Mindfulness, green exercise, and connectedness to nature are increasingly popular topics among academics and the public. These three topics overlap in the underexplored area called mindful green exercise. Mindful green exercise is a blend of mindful exercise and green exercise. Mindful exercise is physical exercise during which people pay attention on purpose without judgment to each new present moment. The person applies an accepting awareness to internal phenomena (thoughts, emotions, and bodily sensations) and external phenomena (objects and events in the environment). Green exercise is exercise performed outdoors in natural environments. Despite its name, green exercise does not only …

Synthesis, Radiolabeling And Evaluation Of A Suite Of Tracers With 44Sc For Detecting Extracellular Dna, Zhiyao Li

McKelvey School of Engineering Theses & Dissertations

Neutrophil extracellular traps involve the rapid translocation of DNA to the outside of the cell under certain stimuli. This structure forms a fibrous network that is able to limit the spread of pathogens and to kill microorganisms. It has also been shown to be present in various pathological processes such as inflammation, autoimmune diseases, and cancer metastasis. Currently, the formation process of NETs in vivo is being extensively studied. However noninvasive detection and quantitation has yet to be achieved. A class of PET tracers are described here that consists of a DNA dye as the backbone that is labeled with …

Online Dashboards For Sars-Cov-2 Wastewater Data Need Standard Best Practices: An Environmental Health Communication Agenda, Colleen C. Naughton, Rochelle H. Holm, Nancy J. Lin, Brooklyn P. James, Ted Smith

Faculty Scholarship

The COVID-19 pandemic has highlighted the benefits of wastewater surveillance to supplement clinical data. Numerous online information dashboards have been rapidly, and typically independently, developed to communicate environmental surveillance data to public health officials and the public. In this study, we review dashboards presenting SARS-CoV-2 wastewater data and propose a path toward harmonization and improved risk communication. A list of 127 dashboards representing 27 countries was compiled. The variability was high and encompassed aspects including the graphics used for data presentation (e.g., line/bar graphs, maps, and tables), log versus linear scale, and 96 separate ways of labeling SARS-CoV-2 wastewater concentrations. …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Anxiety-Like Behavior In C57bl/6j Mice Is Sexually Dimorphic And Altered By Buprenorphine, Ohm Sharma

Chancellor’s Honors Program Projects

No abstract provided.

Excitation Power Dependence Of Blinking In Copper-Indium-Sulfide Quantum Dots, Nicholas Chambers

Physics Undergraduate Honors Theses

Under continuous excitation, quantum dots exhibit random transitions between fluorescent ON states and non-fluorescent OFF states --- a phenomenon known as blinking. A physical description of the mechanism responsible for blinking that applies broadly to many types of quantum dots remains under debate. We study the blinking behavior of the non-toxic CuInS2 quantum dot, a system that has seen little investigation at the single-particle level. In particular, the optical properties of CuInS2 quantum are often improved by adding ZnS to the nanoparticles, but this addition leads to complex structural-optical property relationships that are even less understood. To probe the relationship …

Risk Assessment Framework For Evaluation Of Cybersecurity Threats And Vulnerabilities In Medical Devices, Maureen S. Van Devender

<strong> Theses and Dissertations </strong>

Medical devices are vulnerable to cybersecurity exploitation and, while they can provide improvements to clinical care, they can put healthcare organizations and their patients at risk of adverse impacts. Evidence has shown that the proliferation of devices on medical networks present cybersecurity challenges for healthcare organizations due to their lack of built-in cybersecurity controls and the inability for organizations to implement security controls on them. The negative impacts of cybersecurity exploitation in healthcare can include the loss of patient confidentiality, risk to patient safety, negative financial consequences for the organization, and loss of business reputation. Assessing the risk of vulnerabilities …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

Inaugural Artificial Intelligence For Public Health Practice (Ai4php) Retreat: Ontario, Canada, Jacqueline K. Kueper, Laura C. Rosella, Richard G. Booth, Brent D. Davis, Sarah Nayani, Maxwell J. Smith, Dan Lizotte

Computer Science Publications

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group …

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

On Cox Proportional Hazards Model Performance Under Different Sampling Schemes, Hani Samawi, Lili Yu, Jingjing Yin

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on …

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

The Impact Of Virtual Reality On The Healthcare Industry, Peter Sullivan

Honors Projects in Information Systems and Analytics

Virtual reality (VR) took off in 2013 and has touched many public sectors, from gaming, to business, to healthcare. This study looks at virtual reality's impact has affected the healthcare system, with a focus on its use for medical training, patient recovery, patient pain management, and mental health care. A literature review was conducted on the current state of the industry addressing virtual reality's performance in the field, the perception of experts, and an estimation of financial undertakings. Looking at cost analyses brought a fuller approach to the research. Surveying researchers and workers within the realm of healthcare and VR …

Scholars Day 2023 Program Of Events, Carl Goodson Honors Program

Scholars Day

This is the program of events for the 2023 Scholars Day Conference, where undergraduates across disciplines present their scholarly and creative works.

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Hipaa Vs. Medical Research: Improving Patient Care Through Integration Of Data Privacy And Data Access, Katherine D'Ordine

Honors Projects in Data Science

The purpose of this research is to understand the current relationship between data access and data privacy in the health care industry and attempt to find a way that important health care research can still be conducted amidst HIPAA regulations. There is a lack of extensive research on the impacts of data privacy on health care research due to access regulations, so a survey was created regarding current data processes and recommendations for creating a healthier relationship between privacy and access for research. It was distributed to anyone in health care, analytics, or research to get a variety of perspectives. …