Data Science Commons^™

Beyond Hcahps: Analysis Of Patients’ Comments Provides An Expanded View Of Their Hospital Experiences, Andrew S. Gallan, Rakesh Niraj, Awanindra Singh

Patient Experience Journal

An important concern for health care professionals is that standardized patient surveys may not fully capture all the topics that are important to patients. As a result, health care professionals may not have a complete picture of what their patients experience. The purpose of this research is to utilize a state-of-the-art Natural Language Processing technique to make sense of patients’ solicited, unstructured comments to gain a deeper and broader understanding of their experiences in the hospital. We analyzed a large dataset of inpatient survey responses (48,592 patients generating 65,998 comments) by a patient experience survey vendor for an eleven-hospital health …

An Exploratory Data Analysis On Covid-19 And Its Effects On Crime In New York City, Lanlie Nguyen

Honors Projects

The purpose of this study was to analyze the effects of the COVID-19 pandemic and how it has affected the crime rates present in New York City over the years of 2019 and 2020. There is limited criminal research that investigate the connection to pandemics, and how it can be used to reduce crime rates in similar situations. The goal of this study is to reduce crime rates and provide possible policy implications.

This project analyzes the crime rate trends present before and during the COVID-19 pandemic, and compares it to the number of COVID-19 cases. Analysis of the statewide …

Topological Data Analysis With Mapper, Gretchen Langenbahn

Honors Projects

This project is an introduction and overview of Mapper. Mapper is a method of high dimensional data visualization. Data visualization is a very important part of data analysis as it allows for further interpretation and exploration of data. Visualization of high dimensional data sets can be challenging as each variable is a new dimension that must be represented on a 2D, or at most 3D, graph. Mapper allows for high dimensional visualization by using Topological methods to study the relationships between points. This project goes over two different data set: the Iris data set, and a high dimensional data set …

Exploring Music Genres: A Study Of Optimal Differentiation By Feature, Rebecca Stetler

Honors Projects

This study explores the presence of optimal differentiation in music at the feature level by genre. Popularity prediction models are constructed and used to identify influential features in predicting popularity in each genre. These influential features are then assessed for optimal differentiation of the most popular songs from all songs in the genre.

An Educator’S Perspective Of The Tidyverse, Mine Çetinkaya-Rundel, Johanna Hardin, Benjamin Baumer, Amelia Mcnamara, Nicholas J. Horton, Colin W. Rundel

Statistical and Data Sciences: Faculty Publications

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, “is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next” (Wickham et al. 2019). These shared principles have led to the …

Predicting Covid-19 Fake News, Nilanjana Nambiar

Honors Projects in Data Science

The aim of this project is to use a machine learning model to identify COVID-19 fake news on Twitter and perform additional analysis on the fake news tweets to distinguish any common trends. As misinformation is very common in the information found online, the purpose of the study is to see how machine learning can be used to discern what information can be classified as true versus what is false. Prior research regarding fake news detection, modeling, and analysis was conducted to familiarize on the current studies provided in predicting and analyzing COVID-19 fake news on Twitter. In this study, …

Cancel Culture: Who Or What Will Be Next?, Christine Trumper

Honors Projects in Data Science

This paper utilizes Data Science and Applied Statistic techniques, to perform an analytical dive into Cancel Culture as it is referenced and used on Twitter. The research focuses on analyzing how Cancel Culture has affected the sentiment of Twitter, specifically how it impacts prominent topics in the media that have occurred between February 2021 to September 2021. The development of a topic and sentiment analysis will be based on 1,302,844 Tweets collected using Twitter’s API. Cancel Culture became popularized on social media in the past few years and there is little concrete information regarding its process and the demographics it …

Identifying Factors That Lead To Injury In The Nfl, Matthew Toner

Honors Projects in Data Science

This study hypothesizes that injury-causing factors can be identified through training machine learning models with NFL injury data. The machine learning process entailed web scraping, pre-processing, cleaning, modeling, and analyzing NFL injury data to identify these factors. The features used to model injuries included the following: games played, games started, weight, height, age, year, years of experience, starting position, and team. The four models used to model NFL injuries were Logistic Regression, Decision Trees, Random Forests, and Gradient Boosted Trees. The model with the best performance was the Gradient Boosted Trees model, with an F1 score of 0.508. In addition, …

Twitter's Role In An Increasingly Polarized Political Climate; A Look Into The 2020 Us Elections, Leanne Kendall

Honors Projects in Data Science

Amidst politically strained times, one might wonder what has cause such an exaggerated gap between the views of democrats and republicans. For years, research has suggested the US’s voting population is becoming increasingly politically polarized, with one of the causes being social media. This study's purpose is to understand more about the role that social media plays in the polarization of parties in the US. The study is comprised of the analysis of over 3,000,000 tweets from 9/22/2020 through 11/10/2020 that mention or are written by senate and presidential candidates. Natural language processing, network graphing, and sentiment analyses were utilized …

Social Media Discussion On Covid-19 Impact On Mental Health In The Us, Uk, And India, Weixiao Sun

Honors Projects in Data Science

Discovered in December 2019, Coronavirus (Covid-19) is an infectious disease that has spread rapidly around the world. The World Health Organization (WHO) declared Covid-19 a pandemic in March 2020. The pandemic has increased the occurrence of mental health problems including depression, stress, and anxiety. This research used real-life Tweets collected related to Covid-19 from March 2020 until October 2021. The objective is to analyze Tweets from the US, UK, and India to discover what topics people are discussing about Covid-19's impact on mental health. The theme for the US was related to government and politics, some dominant users in the …

*Interactive Earthquake Visualization With Open Data, Matous Hybl

Campus Research Month

Because earthquakes claim thousands of lives and billions of dollars yearly, there is a great need to recognize patterns in seismic data. While some tools for analysis exist, most geological software is expensive and open earthquake visualizations are limited. In this project, we provide accessible earthquake visualizations aimed to encourage geologists, and science enthusiasts in general, to explore open data using accessible, yet powerful, tools.

Chattanooga Crime Over Time: An Analysis Of Police Incident Open Data, Logan Bateman

Campus Research Month

The police and citizens of Chattanooga may want to know where the most crime occurs, what time of day is crime or police incidents most likely to occur over time. This information can help them understand the crime hotspots in the area. This research work presents a dashboard built upon open data in attempt to bring understanding and insights to the police and citizens about police incidents from the city of Chattanooga over the past five years.

Time Dependent Analysis Of Rat Microglial Surface Markers In Traumatic Brain Injury Reveals Dynamics Of Distinct Cell Subpopulations, Henry W Caplan, Supinder Bedi, Charles S Cox, Scott D Olson

Faculty, Staff and Student Publications

Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to the activation of microglia. Accurate identification of microglial populations is key to understanding therapeutic approaches that modify microglial responses to TBI and improve long-term outcome measures. Notably, previous studies often utilized an outdated convention to describe microglial phenotypes. We conducted a temporal analysis of the response to controlled cortical impact (CCI) in rat microglia between ipsilateral and contralateral hemispheres across seven time points, identified microglia through expression of activation markers including CD45, CD11b/c, and p2y12 receptor and evaluated their activation state using additional …

A Web User Interface Image Processing Tool For Classifying The Extent Of Dementia Across Alzheimer’S, Sathvik Prasad Palyam, Robin Ghosh

ATU Scholars Symposium

Alzheimer's disease (AD) is the most common form of dementia. This project used four image specifications to classify the dementia stages in each patient applying the CNN algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Later, a web interface tool was developed to educate doctors or researchers to check the patients' dementia level based on the MRI brain images and suggest symptoms that strengthen the predicted level of AI. A user uploads the brain scan, which is sent to the backend server, where the image is …

How Students Use The Services Available From Lindenwood Universities Library, Jennifer Sailor

2022 Student Academic Showcase

Student Assessment Scholars took on the Library Services stakeholder proposal. Their goal was to find how students use the service available from Lindenwood Universities Library. Through evidence, it was found that students have a positive sentiment towards the Lindenwood’s Library Services. That the Students want longer hours, better marketing, and find one of the best services being the building itself. Lindenwood Universities Library Services are similar to the other schools in the athletic conference. Lindenwood University houses a Maker Lab, Career Services, and technology rentals like the other universities, but Lindenwood students were not aware of them. Overall, students were …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

The Impact Of Pediatric Opioid-Related Visits On Us Emergency Departments, Angela L Stotts, Lisa Cleveland, Steven Shoptaw, James R Langabeer

Faculty, Staff and Student Publications

BACKGROUND: While there is significant research exploring adults' use of opioids, there has been minimal focus on the opioid impact within emergency departments for the pediatric population.

METHODS: We examined data from the Agency for Healthcare Research, the National Emergency Department Sample (NEDS), and death data from the Centers for Disease Control and Prevention. Sociodemographic and financial variables were analyzed for encounters during 2014-2017 for patients under age 18, matching diagnoses codes for opioid-related overdose or opioid use disorder.

RESULTS: During this period, 59,658 children presented to an ED for any diagnoses involving opioids. The majority (68.5%) of visits were …

Mapping The Impact Of A Trailway System On The Amount Of Trash Present Within Two Watersheds Of Lynchburg City, Virginia, Lillian Smith

Student Scholar Showcase

Transportation of trash debris within water systems is a prominent occurrence which has been linked to natural and artificial processes such as wind, rain, and littering. Recreational areas, such as activities along greenway trails, have been determined to be a source of debris found in waterways. This study examines whether the presence of an established recreational trail system limits trash accumulation in the entirety of a watershed. Trash data collected at Blackwater Creek, which contains an established trail system, was compared to trash data collected at Fishing Creek, containing a non-established trail system, to answer this hypothesis. A distance of …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Privacy-Preserving Logistic Regression With Secret Sharing

Faculty, Staff and Student Publications

BACKGROUND: Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.

METHODS: In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton-Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority …