Multiple Structural Breaks Detection through Genetic Algorithm Under stimuli and workloads, the human body tends to display discomforts and covert proximal cognitions that can manifest through physiological responses. One such response can be in a form of skin sweats, easily capturable via wearable sensors. The sensors capturing electrodermal activities (EDA) record big data serially (4Hz, 8Hz), per subject, over a duration of an experiment (approximately 35 min). This project aims to study serial EDA data collected on subjects from an experiment being conducted in neuropsychology at the University of Iowa, with the goal to detect structural breaks corresponding to epochs of learning activities and assess the role that biofeedback plays in efforts to engage people in a learner space. The project mobilizes technological innovations in neuroimaging (fNIRS), wearable sensors monitoring covert cognitive activity, monitoring arousal states under workload, and video data outputs to address learners’ emotional discomfort. The project will focus on structural breaks detection though a genetic algorithm stochastic search across the spectrum of EDA data.
An Investigation of the role of GLP-1 Receptors in Vision Loss: The Case of NAION patients Glucagon-like peptide-1 (GLP-1) receptor agonists are a class of anorectic drugs that help reduce blood sugar and energy intake by activating the GLP-1 receptor. GLP-1 receptor agonists are being used for weight loss and diabetes and are becoming the most widely used medications for these conditions. Recent research provided some evidence of an increased risk of non-arteritic anterior ischemic optic neuropathy (NAION) in patients using GLP-1 receptor agonists. NAION is a condition that is easily associated with the risk of a permanent vision loss. However, clinicians are not settled on the underlying causal mechanism of NAION. Although there have been suggestions that NAION conditions are linked to abrupt decrease in blood flow toward the optic nerve, there appears to be a paucity of research work in support of this hypothesis. Is there any viable association between NAION and GLP-1 receptor agonists? Are subjects under GLP-1 more likely to develop visual loss? The current research study will embark on an exploratory retrospective chart review of the record of n = 400 NAION patients, their pattern of vision loss if ever, their visual imaging data, their pattern of GLP-1 receptor agonists usage or lack thereof, their health history, their diabetics and obesity status, and other prognostic factors that can contribute toward elucidating on time to visual loss in both with or without GLP-1 receptor agonists usage. The project will mix time to event modeling and image analysis to explore this potential association amidst a host of prognostic factors.
Burnout Among Servicemen: A case of the Russian-Ukraine War Studies of war veterans estimated that 95% of military members consider burnout to be the leading cause of separation, retirement, and interpersonal disorders. Burnout, defined as a syndrome of emotional exhaustion and feelings of workplace failure that occurs in response to chronic exposure to occupational stressors, if not attended to and properly addressed, can trigger, or induce emotional disorders, feeling of discouragement, frustrations, worthlessness, and depression among servicemen. The current project involves researchers from the University of Iowa and investigators in the Psychology Department at Tara Shevchenko University in Kiev, Ukraine. The data were gathered on Ukraine’s servicemen at the frontline of the Russian-Ukraine war (n = 400). Burnout Assessment Tools (BAT), Interpersonal Guilt Rating Scale Self Report (IGRS-SR), Basic Psychological Need Satisfaction and Frustration Scale measurements (BPNSFS), will be cross-studied as functions of servicemen socio-demographic characteristics such as: gender, age, education, marital status, number of children, combat operation and other characteristics. Specific hypotheses will be studied and tested, and recommendations will be made with direct policy implications regarding burnouts among frontline servicemen.
Evaluate Barriers that Audiologists Perceive to Providing Care Under Medicaid Purpose: To evaluate barriers that audiologists perceive to providing care under Medicaid and characterize audiologists’ attitudes toward Medicaid. Introduction: We will evaluate what barriers audiologists perceive to participating in Medicaid insurance programs. Our goal is to ascertain if barriers to Medicaid participation stem from logistic challenges or provider disposition challenges. Logistic challenges relate to the logistics of accepting Medicaid insurance such as complicated enrollment procedures, excessive pre-authorization requirements, or delayed payment for services provided. Dispositional challenges relate to attitudes and habits of audiologists such as negative perceptions about patients with Medicaid insurance or opposition to means-based federal health insurance programs entirely. It is important to fully characterize both types of barriers, as each will respond to different mechanisms to increase access through wider networks of participating providers. Participants:Starting in April,we will invite audiologists with active state Audiology licenses to practice in the United States to participate. Given our focus on access to hearing aids for pediatric patients, we will exclude audiologists who only see adult patients. Audiologists who see both adult and pediatric patients will be invited to participate, as well as audiologists who see pediatric patients only. The survey will close in May. Methods: We created a survey instrument that examines what barriers Audiologists perceive to participating in state Medicaid insurance programs. The survey is adapted from Logan et al. (2015), which integrated items from the Social Responsibility Scale and the Perceived Barrier Scale. The Social Responsibility Scale was first developed by Dharamsi et al. (2007) and uses a 7-point Likert scale (from “strongly agree” to “strongly disagree”). Logan et al. (2015) developed the Perceived Barrier Scale with items drawn from previous work in the dental field. It uses a 5-point Likert scale (from “not important” to “very important”). We adjusted verbiage to reflect audiology practices and incorporate items based on previous audiology research in Medicaid access. In addition, we will collect demographic characteristics about survey participants and their practices (e.g., type of facility, payor mix, patient volumes). There will be specific questions about their current Medicaid enrollment status and their billing arrangement (e.g., individual NPI or a hospital based NPI). Data analyses: We will treat Medicaid participation status as a dichotomous, dependent variable of interest. Independent variables will include type of practice, years of practice, region, race and ethnicity, specialty (pediatric only versus pediatric and adult). We will use logistic regression methods to examine the relationship between items on the Perceived Barriers and Social Responsibility scales with participation in Medicaid. As an exploratory integration, we will visually represent the respondents and trends in the context of a heatmap produced in a different project to examine provider density.
Trends in Lyme Disease Detection and Treatment Lyme disease (LD) is a growing public health problem nationwide, as both the vector species (ticks) and the pathogen (borellia Sp.) spread. Iowa specifically has experienced fluctuating incidence, with levels higher than historical norms, since around 2010. Prompt treatment of LD is important, with delays in treatment being associated with a variety of disseminated symptoms and considerable morbidity. Nevertheless, diagnosis and treatment of LD is heterogeneous, and may vary geographically and by provider. Patients from different socioeconomic backgrounds may also receive care at different rates and times. In this project, we will examine LD diagnosis and treatment in Iowa at a provider and county level, examining whether providers are changing practices over time, and whether or not there are regional differences in treatment and outcomes. We will use a large database of medical claims data to understand these complex processes.
Deep Learning to Predict Enrollment at the University of Iowa The University of Iowa, like many large institutions, has substantial data infrastructure regarding prospective, current, and former students. This resource is actively used by researchers in the Department of Biostatistics to help the University with recruitment, planning, resource allocation, and promoting student success and retention. This project will seek to expand the methods used to predict enrollment at the University by implementing recurrent neural network (RNN) models, and applying them to time-series of real student-level data. We will explore neural networks generally, as well as the use of graphics processing unit (GPU) accelerators and high-performance computing (HPC) resources to improve computation time.