Other Medicine and Health Sciences Commons^™

Hgs-3 The Influence Of A Tandem Cycling Program In The Community On Physical And Functional Health, Therapeutic Bonds, And Quality Of Life For Individuals And Care Partners Coping With Parkinson’S Disease, Leila Djerdjour, Jennifer L. Trilk

SC Upstate Research Symposium

Purpose Statement: Several studies have shown that aerobic exercise can have a positive impact on alleviating symptoms experienced by individuals with Parkinson's disease (PD). Despite this evidence, the potential benefits of exercise for both PD patients and their care partners (PD dyad) remain unexplored. This research project investigates the effectiveness, therapeutic collaborations, and physical outcomes of a virtual reality (VR) tandem cycling program specifically designed for PD dyads.

Methods: Following approval from the Prisma Health Institutional Review Board, individuals with PD were identified and screened by clinical neurologists. The pre-testing measures for PD dyads (N=9) included emotional and cognitive status …

Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed

Marcus Institute of Integrative Health Faculty Papers

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.

MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging …

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. …

Clinical Effects Of Lactobacillus Reuteri Probiotic In The Treatment Of Chronic Periodontitis: A Systematic Review Of Randomized Controlled Trials, Josephine Ram, Shilpa Bhandi, Kamran H. Awan, Frank Licari, Shankargouda Patil

Annual Research Symposium

No abstract provided.

Investigating The Use Of Conversational Agents As Accountable Buddies To Support Health And Lifestyle Change, Ekaterina Uetova, Dympna O'Sullivan, Lucy Hederman, Robert J. Ross

Academic Posters Collection

The poster focuses on the role of conversational agents in promoting health and well-being. Results of the literature review indicate that negative emotions can hinder individuals from taking necessary actions related to their health. The study concludes that understanding and addressing emotional barriers is essential to facilitating early access to health services and improving well-being. The poster outlines plans to investigate motivation strategies, develop a prototype conversational agent based on user study insights and chat log data, and incorporate emotion regulation to effectively manage users' emotional experiences.

Thermodynamic Analysis Of Digestate Pyrolysis Coupled With Co2 Sorption, Antonella Dimotta, Cesare Freda

Conference papers

To date the management of digestate is a crucial task for anaerobic digestion process. In the present work a strategy for digestate management is thermodynamically analyzed by a commercial software for process simulation called CHEMCAD®. Pyrolysis of digestate is simulated by a minimization of the free Gibbs energy. The sequestration of the carbon dioxide (CO2) released by the pyrolysis is investigated by the addition of calcium oxide, in order to reduce CO2 emissions. The effect of the pyrolysis temperature between 400–900 °C and of the CaO/digestate mass ratio between 0–0.5 was discussed, as well. The CHEMCAD application allowed to investigate …

A Comparison Of Statistical Methods For Modeling Count Data With An Application To Hospital Length Of Stay, Gustavo Fernandez, Kristina Vatcheva

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Background

Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), …

A Comparative Study On Deep Learning Models For Text Classification Of Unstructured Medical Notes With Various Levels Of Class Imbalance, Hongxia Lu, Louis Ehwerhemuepha, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical …

A Push For Inclusive Data Collection In Stem Organizations, Nicholas P. Burnett, Alyssa M. Hernandez, Emily E. King, Richelle L. Tanner, Kathryn Wilsterman

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Professional organizations in STEM (science, technology, engineering, and mathematics) can use demographic data to quantify recruitment and retention (R&R) of underrepresented groups within their memberships. However, variation in the types of demographic data collected can influence the targeting and perceived impacts of R&R efforts - e.g., giving false signals of R&R for some groups. We obtained demographic surveys from 73 U.S.-affiliated STEM organizations, collectively representing 712,000 members and conference-attendees. We found large differences in the demographic categories surveyed (e.g., disability status, sexual orientation) and the available response options. These discrepancies indicate a lack of consensus regarding the demographic groups that …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

Investigating The Uncertainties In Ct Non-Small Cell Lung Cancer Radiomics, Gary Ge

Theses and Dissertations--Radiation Medicine

Radiomics is a technique that extracts quantitative features, termed radiomic features, from medical images using data-characterization algorithms. These radiomic features can be used to identify tissue characteristics and radiologic phenotyping that are not observable by clinicians in a non-invasive, low-cost manner, potentially generating image biomarkers for clinical decision. To date, there are still many uncertainties involved in radiomics which limit its clinical implementation. Herein, we propose to explore the impact of each component in the radiomics pipeline on predicting clinical outcomes. In Chapter II, we conduct a thorough review of CT lung cancer radiomics studies to examine the typical feature …

Application Of Competitive Intelligence For Insular Territories: Automatic Analysis Of Scientific And Technology Trends To Fight The Negative Effects Of Climate Change, Henri Dou, Pierre Fournie

International Journal of Islands Research

Islands are fragile territories because of their geographical position. As a result, climate impacts can have serious consequences, of which some are irreversible. Therefore, it is necessary to allow insular territories to benefit from the latest scientific and technological advances in combating climate effects. The current article shows how to deal with automatic analysis of scientific information on the one hand, but also its applications via patents. We will analyse the latest scientific results as well as their possible applications using patent analysis. We will also focus on experts, laboratories, and leading companies, that are active on the field. The …

Covid-19 Impact On Radiology Students’ Distance Learning (Summer 2021), Mary Lee, Jason Chan, Cheryann Jackson-Holmes, Renzo Marmolejo, Zoya Vinokur

Publications and Research

The Radiological Technology students have adjusted from the urgent distance learning that was enacted in the Spring of 2020, to the hybrid distance learning that is currently in place. This hybrid distance learning is the same way the incoming class of radiological technology students were taught. Both cohorts of students were tracked over the year by online anonymous surveys. We wanted to know how students were adapting to distance learning, if their focus and motivation varied over the course of the year due to changing pandemic conditions. For the students that were working, what impact did it have on their …

Stem Education In College: An Analysis Of Stakeholders’ Recent Challenges And Potential Solutions, Santanu De, Georgina Arguello

FDLA Journal

A vast majority of academic disciplines and curricula in the college center around Science, Technology, Engineering, and Mathematics (STEM), which are critical to developing the skills necessary for a global workforce. Rapid changes in pedagogical setups, educational modes, and advances in instructional technology entail diverse challenges for key stakeholders (i.e. students, faculty, and the organizations). This paper highlights the most relevant challenges and potential solutions in STEM higher education at the college level, reported in the last decade. The holistic analysis combining the three stakeholders’ perspectives would help elucidate significant contemporary aspects impacting the fields. The goal is to further …

A Neutrosophic Clinical Decision-Making System For Cardiovascular Diseases Risk Analysis, Florentin Smarandache, Shaista Habib, Wardat-Us- Salam, M. Arif Butt, Muhammad Akram

Branch Mathematics and Statistics Faculty and Staff Publications

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross-sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially …

Equivalency Testing For Two Formulations Of A Clinical Laboratory Control Material, Jessica M. Hart

Capstone Experience

Clinical laboratory control materials are an integral part of legally-mandated and highly regulated quality control protocols in all clinical laboratories. These controls ensure accurate performance of the laboratory testing and instrumentation used to produce medical test results for millions of patients. It is of clinical and public health interest to ensure the diagnostic test results which affect so many people are regulated by the most accurate and precise controls.

Formulation changes in control materials have the potential to impact laboratory quality control. In this study, data from two formulations of a hematology control were compared to assess equivalency of the …

How Data Is Changing The World Of Healthcare, Cameron Marous

Honors Capstone Enhancement Presentations

No abstract provided.

The Effectiveness Of Transfer Learning Systems On Medical Images, James Boit

Masters Theses & Doctoral Dissertations

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned …

Modulation Of Medical Condition Likelihood By Patient History Similarity, Jonathan Turner, Dympna O'Sullivan, Jon Bird

Articles

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group.

Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest …

Ecological Determinants Of Respiratory Health: Examining Associations Between Asthma Emergency Department Visits, Diesel Particulate Matter, And Public Parks And Open Space In Los Angeles, California, Jason A. Douglas, Reginald S. Archer, Serena E. Alexander

Health Sciences and Kinesiology Faculty Articles

Los Angeles County (LAC) low-income communities of color experience uneven asthma rates, evidenced by asthma emergency department visits (AEDV). This has partly been attributed to inequitable exposure to diesel particulate matter (DPM). Promisingly, public parks and open space (PPOS) contribute to DPM mitigation. However, low-income communities of color with limited access to PPOS may be deprived of associated public health benefits. Therefore, this novel study investigates the AEDV, DPM, PPOS nexus to address this public health dilemma and inform public policy in at-risk communities. Optimized Hotspot Analysis was used to examine geographic clustering of AEDVs, DPM, and PPOS at the …

The Validity Of Online Patient Ratings Of Physicians, Jennifer L. Priestley, Yiyun Zhou, Robert Mcgrath

Jennifer L. Priestley

Background: Information from ratings sites are increasingly informing patient decisions related to health care and the selection of physicians.

Objective: The current study sought to determine the validity of online patient ratings of physicians through comparison with physician peer review.

Methods: We extracted 223,715 reviews of 41,104 physicians from 10 of the largest cities in the United States, including 1142 physicians listed as “America’s Top Doctors” through physician peer review. Differences in mean online patient ratings were tested for physicians who were listed and those who were not.

Results: Overall, no differences were found between the online patient ratings based …

The Effect Of Arm Swing On Countermovement Vertical Jump Performance, Arash Mohammadzadeh Gonabadi

UNO Student Research and Creative Activity Fair

Vertical jumping is one of the popular ways to evaluate ankle-knee efficiency in athletic population. Arm swing can play a crucial role in enhancing vertical jump performance. This study aimed to address the differences in kinetic and kinematic parameters during countermovement jump motion with arm swing (AS) and no arm swing (NAS). We used OpenSim to examine the efficacy of AS in reducing the impulse applied to the body and changes in range of lower limb joint angles at landing instant. We calculated the maximum vertical peak of the ground reaction force and impulse generated at landing in two different …

Bench Tracker: Improving Actionable Insights In Smartwatch Fitness Application By Increasing Usability Through Simplification, Chris Campanelli

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

This thesis describes a smartwatch solution, called Bench Tracker for fitness monitoring using Apple Watches and Apple iPhone devices. The system involves a mobile based application that allows users to track and monitor bench press workouts in real-time to create actionable insights. By creating actionable insights on a smartwatch application, and improving the application’s usability through simplification, users agreed they would use the fitness application created that specifically tracked bench presses. A leading fitness app was used as the comparator, and it was discovered that users were undecided if they would use this app for bench press tracking. This paper …

The Chapman Bone Algorithm: A Diagnostic Alternative For The Evaluation Of Osteoporosis, Elise Levesque, Anton Ketterer, Wajiha Memon, Cameron James, Noah Barrett, Cyril Rakovski, Frank Frisch

Mathematics, Physics, and Computer Science Faculty Articles and Research

Osteoporosis is the most common metabolic bone disease and goes largely undiagnosed throughout the world, due to the inaccessibility of DXA machines. Multivariate analyses of serum bone turnover markers were evaluated in 226 Orange County, California, residents with the intent to determine if serum osteocalcin and serum pyridinoline cross-links could be used to detect the onset of osteoporosis as effectively as a DXA scan. Descriptive analyses of the demographic and lab characteristics of the participants were performed through frequency, means and standard deviation estimations. We implemented logistic regression modeling to find the best classification algorithm for osteoporosis. All calculations and …

Development And Implementation Of A Homogeneous And A Heterogeneous Anthropomorphic End To End Quality Assurance Audit System Phantom For Magnetic Resonance Guided Radiotherapy Modalities Ranging From 0.35 T To 1.50 T, Angela Steinmann

Dissertations & Theses (Open Access)

Introduction: Magnetic resonance (MR) guided radiation therapy (MRgRT) is an emerging field that integrates an MR imager with either a linear accelerator or three radioactive cobalt-60 sources. Before institutions participate in multi-institutional NCI-sponsored clinical trials, they are required to perform a credentialing test provided by IROC-Houston. During the credentialing test, end-to-end phantoms are used to evaluate the institution’s ability to perform consistent and accurate radiation treatments. IROC-Houston’s conventional anthropomorphic phantoms are not visible in MR, thus they are insufficient for MRgRT systems. The purpose of this work was to create an anthropomorphic thorax and a head and neck (H&N) phantom …

The Validity Of Online Patient Ratings Of Physicians, Jennifer L. Priestley, Yiyun Zhou, Robert Mcgrath

Faculty and Research Publications

Background: Information from ratings sites are increasingly informing patient decisions related to health care and the selection of physicians.

Objective: The current study sought to determine the validity of online patient ratings of physicians through comparison with physician peer review.

Methods: We extracted 223,715 reviews of 41,104 physicians from 10 of the largest cities in the United States, including 1142 physicians listed as “America’s Top Doctors” through physician peer review. Differences in mean online patient ratings were tested for physicians who were listed and those who were not.

Results: Overall, no differences were found between the online patient ratings based …

Ferrocenylchalcone-Uracil Conjugates: Synthesis And Cytotoxic Evaluation, Amandeep Singh, Vishu Mehra, Neda Sadeghiani, Saghar Mozaffari, Keykavous Parang, Vipan Kumar

Pharmacy Faculty Articles and Research

Huisgen’s azide-alkyne cycloaddition reaction was employed to synthesize a series of 1H-1,2,3-triazole-tethered uracil-ferrocenyl chalcone conjugates with the aim of evaluating their in vitro anti-proliferative efficacy on human leukemia (CCRF-CEM) and human breast adenocarcinoma (MDA-MB-468) cell lines. Cytotoxic evaluation studies identified a number of synthesized conjugates that inhibited the proliferation of leukemia cancer cells by ~70% after 72 h. The selected synthesized conjugates were found to be significantly less cytotoxic against normal kidney cell line (LLC-PK1) when compared with CCRF-CEM cancer cells.