Data Science Commons^™

Semirecumbent Positioning During Anesthesia Recovery And Postoperative Hypoxemia: A Randomized Clinical Trial, Xinghe Wang, Kedi Guo, Jia Sun, Yuping Yang, Yan Wu, Xihui Tang, Yuqing Xu, Qingsong Chen, Si Zeng, Liwei Wang, Su Liu

Faculty, Staff and Student Publications

IMPORTANCE: The efficacy of a semirecumbent position (SRP) in reducing postoperative hypoxemia during anesthesia emergence is unclear despite its widespread use.

OBJECTIVE: To determine the differences in postoperative hypoxemia between patients in an SRP and a supine position.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was performed at a tertiary hospital in China between March 20, 2021, and May 10, 2022. Patients scheduled to undergo laparoscopic upper abdominal surgery under general anesthesia were enrolled. Study recruitment and follow-up are complete.

INTERVENTIONS: Patients were randomized to 1 of the following positions at the end of the operation until leaving the …

Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu

Master's Theses

In the era of total digitization of documents, navigating vast and heterogeneous data landscapes presents significant challenges for effective information retrieval, both for humans and digital agents. Traditional methods of knowledge organization often struggle to keep pace with evolving user demands, resulting in suboptimal outcomes such as information overload and disorganized data. This thesis presents a case study on a pipeline that leverages principles from cognitive science, graph theory, and semantic computing to generate semantically organized knowledge graphs. By evaluating a combination of different models, methodologies, and algorithms, the pipeline aims to enhance the organization and retrieval of digital documents. …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Katarzyna Wasielewska, Rasmus Bro, David Kotz

Dartmouth Scholarship

There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Radiogenomics-Based Risk Prediction Of Glioblastoma Multiforme With Clinical Relevance, Xiaohua Qian, Hua Tan, Xiaona Liu, Weiling Zhao, Michael D Chan, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed …

Aromatic-Carbonyl Interactions As An Emerging Type Of Non-Covalent Interactions, Chaowei Yin, Hebo Ye, Yu Hai, Hanxun Zou, Lei You

Faculty, Staff and Student Publications

Aromatic-carbonyl (Ar···C═O) interactions, attractive interactions between the arene plane and the carbon atom of carbonyl, are in the infancy as one type of new supramolecular bonding forces. Here the study and functionalization of aromatic-carbonyl interactions in solution is reported. A combination of aromatic-carbonyl interactions and dynamic covalent chemistry provided a versatile avenue. The stabilizing role and mechanism of arene-aldehyde/imine interactions are elucidated through crystal structures, NMR studies, and computational evidence. The movement of imine exchange equilibria further allowed the quantification of the interplay between arene-aldehyde/imine interactions and dynamic imine chemistry, with solvent effects offering another handle and matching the electrostatic …

Antibiotic Bone Cement Accelerates Diabetic Foot Wound Healing-Elucidating The Role Of Rock1 Protein Expression, Liang Li, Guangming Zhang, Yanwei Sun

Faculty, Staff and Student Publications

No abstract provided.

Associations Between Longer Leukocyte Telomere Length And Increased Lung Cancer Risk Among Never Smokers In Urban China, Jason Y Y Wong, Xiao-Ou Shu, Wei Hu, Batel Blechter, Jianxin Shi, Kevin Wang, Richard Cawthon, Qiuyin Cai, Gong Yang, Mohammad L Rahman, Bu-Tian Ji, Yutang Gao, Wei Zheng, Nathaniel Rothman, Qing Lan

Faculty, Staff and Student Publications

BACKGROUND: The complex relationship between measured leukocyte telomere length (LTL), genetically predicted LTL (gTL), and carcinogenesis is exemplified by lung cancer. We previously reported associations between longer pre-diagnostic LTL, gTL, and increased lung cancer risk among European and East Asian populations. However, we had limited statistical power to examine the associations among never smokers by gender and histology.

METHODS: To investigate further, we conducted nested case-control analyses on an expanded sample of never smokers from the prospective Shanghai Women's Health Studies (798 cases and 792 controls) and Shanghai Men's Health Studies (161 cases and 162 controls). We broke the case-control …

Enhancing Foot Care Education And Support Strategies In Adults With Type 2 Diabetes, Hsiao-Hui Ju, Madelene Ottosen, Jeffery Alford, Jed Jularbal, Constance Johnson

Faculty, Staff and Student Publications

BACKGROUND: People with diabetes are susceptible to serious and disabling foot complications, which increase their morbidity and mortality rates. Examining the perspectives of people with diabetes on their foot care routines could help elucidate their beliefs and offer practical ways to prevent foot problems.

PURPOSE: We explored the perspectives of adults with diabetes on their foot care practices to identify and enhance foot care education and support strategies.

METHODOLOGY: Using the Zoom platform, 29 adults with diabetes completed a 3-month telehealth educational program, during which interviews were conducted. This article reports the results of thematic content analysis of the qualitative …

Advances In The Integration Of Microalgal Communities For Biomonitoring Of Metal Pollution In Aquatic Ecosystems Of Sub-Saharan Africa, Mary Mulenga, Concillia Monde, Todd Johnson, Kennedy O Ouma, Stephen Syampungani

Faculty, Staff and Student Publications

This review elucidated the recent advances in integrating microalgal communities in monitoring metal pollution in aquatic ecosystems of sub-Saharan Africa (SSA). It also highlighted the potential of incorporating microalgae as bioindicators in emerging technologies, identified research gaps, and suggested directions for further research in biomonitoring of metal pollution. Reputable online scholarly databases were used to identify research articles published between January 2000 and June 2023 for synthesis. Results indicated that microalgae were integrated either individually or combined with other bioindicators, mainly macroinvertebrates, macrophytes, and fish, alongside physicochemical monitoring. There was a significantly low level of integration (< 1%) of microalgae for biomonitoring aquatic metal pollution in SSA compared to other geographical regions. Microalgal communities were employed to assess compliance (76%), in diagnosis (38%), and as early-warning systems (38%) of aquatic ecological health status. About 14% of biomonitoring studies integrated microalgal eDNA, while other technologies, such as remote sensing, artificial intelligence, and biosensors, are yet to be significantly incorporated. Nevertheless, there is potential for the aforementioned emerging technologies for monitoring aquatic metal pollution in SSA. Future monitoring in the region should also consider the standardisation and synchronisation of integrative biomonitoring and embrace the "Citizen Science" concept at national and regional scales.

Increased Incidence Of Vestibular Disorders In Patients With Sars-Cov-2, Lawrance Lee, Evan French, Daniel H Coelho, Nauman F Manzoor, Adam B Wilcox, Adam M Lee, Alexis Graves, Alfred Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E Williams, Andrew Southerland, Andrew T Girvin, Anita Walden, Anjali A Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J W Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L Rutter, Julie A Mcmurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M Walters, Ken Wilkins, Kenneth R Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B Palchuk, Melissa A Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A Francis, Penny Wung Burgoon, Peter Robinson, Philip R O Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A Moffitt, Richard L Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T Miller, Saiju Pyarajan, Sam G Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T O'Neil, Soko Setoguchi, Stephanie S Hong, Steve Johnson, Tellen D Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang

Faculty, Staff and Student Publications

OBJECTIVE: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population.

STUDY DESIGN: Retrospective.

SETTING: Clinical data in the National COVID Cohort Collaborative database (N3C).

METHODS: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period.

RESULTS: Patients testing positive for COVID-19 were significantly more likely to have …

Listening For Lemurs: Translating Black-And-White Ruffed Lemur (Varecia Variegata) Vocalizations Into Conservation Insights Through Acoustic Monitoring, Carly H. Batist

Dissertations, Theses, and Capstone Projects

The field of bioacoustic monitoring has undergone a significant evolution in recent years, driven by technological innovations that have revolutionized how researchers study animal vocalizations. Traditionally, bioacoustics was rooted in active acoustic monitoring (AAM), involving human observers using recorders in the field to study animal sounds and understand species' vocal communication. However, the emergence of passive acoustic monitoring (PAM) has introduced a new complementary approach, utilizing specialized recorders placed in ecosystems to autonomously capture sounds at wide spatial and temporal scales. My dissertation adopts a translational approach to bioacoustic monitoring, integrating both AAM and PAM techniques to study and survey …

Three Essays Applying Dynamic Models In Economics, Finance, And Machine Learning, Lucas C. Dowiak

Dissertations, Theses, and Capstone Projects

This dissertation is a composition in three parts. Collectively, these essays investigate dynamic methods and their application in the fields of Economics, Finance, and Machine Learning. It pulls liberally from all three. In particular, this dissertation makes repeated use of multi-state modeling frameworks popular in Economics to bring a faceted view to the underlying data and detect its hidden heterogeneity. The challenge of modeling financial assets and estimating their dependence is another focus. For stimulus, concepts in the Machine Learning field are brought in to aid or compete with established econometric techniques.

Econometric Applications of the Hierarchical Mixture-of-Experts

In this …

The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam

Dissertations, Theses, and Capstone Projects

In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …

Contrastive Filtering And Dual-Objective Supervised Learning For Novel Class Discovery In Document-Level Relation Extraction, Nicholas Hansen

Master's Theses

Relation extraction (RE) is a task within natural language processing focused on the classification of relationships between entities in a given text. Primary applications of RE can be seen in various contexts such as knowledge graph construction and question answering systems. Traditional approaches to RE tend towards the prediction of relationships between exactly two entity mentions in small text snippets. However, with the introduction of datasets such as DocRED, research in this niche has progressed into examining RE at the document-level. Document-level relation extraction (DocRE) disrupts conventional approaches as it inherently introduces the possibility of multiple mentions of each unique …

Modeling The Effect Of Population Size On Banking Transaction Channels In Nigeria: Grey Box Vs Support Vector Regression, Desmond Bartholomew, Ngozi P. Olewuezi, Chrysogonus C. Nwaigwe, Felix C. Akanno

CBN Journal of Applied Statistics (JAS)

This study investigates the effect of Nigeria’s population on four selected banking transaction channels. The Nigerian projected population (2022-2027) was used as an input variable for forecasting future volumes of transactions for each channel. The results show that the Support Vector Regression (SVR) model best fits the ATM, Online, and USSD channels of transaction while the Grey-box was better for POS. The forecast results show that ATM, online, and USSD channels had their highest volume of transactions in 2023, while for POS, the highest volume was recorded in 2027. Further results indicate that online and POS transactions would dominate payment …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Cyberbullying Detection On Twitter Data Using Machine Learning Classifiers, Pradip Dhakal

Data Science and Data Mining

This study compares some of the popular machine learning techniques like Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting to classify the tweets into three different categories: cyberbullying based on religion, cyberbullying based on ethnicity, or no cyberbullying. First, various data-cleaning approaches are used to clean the tweet data. After the data is clean and ready, the word embedding techniques, such as a bag of words and term frequency-Inverse document frequency, are used to convert the words into mathematical vectors. Finally, the model will be fitted using the combination of the above-mentioned word embedding techniques and machine …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

Internet-Based Data Platforms Re-Define The Distributions Of Some Large Crabronid Wasps In Arkansas (Hymenoptera: Crabronidae), David E. Bowles

Insecta Mundi

The geographic distributions of three large wasps, Sphecius speciosus (Drury), Stictia carolina Fabricius, and Stizus brevipennis Walsh (Hymenoptera: Crabronidae), occurring in Arkansas are defined using museum specimens and three internet-based data platforms. The internet-based data platforms generally provided more county location records than museum records. Using data from internet sources for easily identified species can better serve to illustrate the known distributions for some species thus making for a powerful tool elucidating distributional patterns and conservation planning.

ZooBank registration. urn:lsid:zoobank.org:pub:DCAE9192-1765-40CD-952B-0A094F413991

Try It Together - Qualitative Coding With Atlas.Ti, Danping Dong, Bryan Leow

2024 AI for Research Week

This hands-on session introduces Atlas.ti, a well-established qualitative data analysis tool for analyzing your transcripts and textual data. The session will cover coding data, extracting insights, creating visualizations, and exploring the tool's latest AI features.

Try It Together: Transcribing Your Audio With Whisper Api, Bella Ratmelia

2024 AI for Research Week

In this hands-on session, we will explore using the Whisper API to transcribe audio recordings from interviews, focus groups, and speeches. The session will delve into best practices and address common issues that may arise during the transcription process.

Exploring The Tradeoff Between Data Privacy And Utility With A Clinical Data Analysis Use Case, Eunyoung Im, Hyeoneui Kim, Hyungbok Lee, Xiaoqian Jiang, Ju Han Kim

Faculty, Staff and Student Publications

BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

METHODS: Predictive modeling of emergency department length of stay was used as …

Risk Factors For Pediatric Ischemic Stroke And Intracranial Hemorrhage: A National Electronic Health Record Based Study, Stuart Fraser, Samantha M Levy, Amee Moreno, Gen Zhu, Sean Savitz, Alicia Zha, Hulin Wu

Faculty, Staff and Student Publications

BACKGROUND: Stroke is an important cause of morbidity in pediatrics. Large studies are needed to better understand the epidemiology, pathogenesis and risk factors associated with pediatric stroke. Large administrative datasets can provide information on risk factors in perinatal and childhood stroke at low cost. The aim of this hypothesis-generating study was to use a large administrative dataset to assess for prevalence and odds-ratios of rare exposures associated with pediatric stroke.

METHODS: The data for patients aged 0-18 with a diagnosis of either ischemic stroke or intracranial hemorrhage were extracted from the Cerner Health Facts EMR Database from 2000 to 2018. …

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma

SMU Data Science Review

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.