Engineering Commons^™

Advanced Air Quality Management With Machine Learning, Cheng-Pin Kuo

Doctoral Dissertations

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations

Inspired by more available and overwhelming data, machine learning …

Biocybersecurity And Deterrence: Hypothetical Rwandan Considerations, Issah Samori, Gbadebo Odularu, Lucas Potter, Xavier-Lewis Palmer

Community & Environmental Health Faculty Publications

Digitalization and sustainability are popular words within modern disciplines as practitioners each look toward the future of their respective fields. Specifically for the African continent, which is making great strides in developmental targets, those two terms are central to core aspects of policy initiatives that may foster cooperation across its varied lands and nations. One of the underlying challenges that confront Africa is a lack of strong regional integration across socioeconomic and political programs; there is value in African regions having more regional connectedness. We assess the rate of regional integration and development in Africa and discuss how to alleviate …

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold …

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Clinical Research In Pneumonia: Role Of Artificial Intelligence, Timothy L. Wiemken, Robert R. Kelley, William A. Mattingly, Julio A. Ramirez

The University of Louisville Journal of Respiratory Infections

No abstract provided.

Biomedical Informatics Applications For Precision Management Of Neurodegenerative Diseases, Justin B. Miller, Guogen Shan, Joseph Lombardo, Gustavo Jimenez-Maggoria

Public Health Faculty Publications

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary …

Comparing Machine Learning And Logistic Regression Methods For Predicting Hypertension Using A Combination Of Gene Expression And Next-Generation Sequencing Data, Elizabeth Held, Joshua Cape, Nathan L. Tintle

Faculty Work Comprehensive List

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically …