Data Science Commons^™

Health And Healthcare: Designing For The Social Determinants Of Health And Blue Zones In North Nashville, Rebecca Tonguis, Honor Thomas, Olivia Hobbs

Belmont University Research Symposium (BURS)

Owned by North Nashville’s First Community Church, a now empty site in the Osage-North Fisk neighborhood of North Nashville has been identified as a potential site for a new location of The Store, in addition to a community-centric architectural development based on the social determinants of health and informed by the principles behind Blue Zones, the locations with the highest lifespans in the world. Opened by Brad Paisley and Kimberly Williams-Paisley, The Store is a free grocery store that “allow[s] people to shop for their basic needs in a way that protects dignity and fosters hope”, for which North Nashville …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

The Psychological Science Accelerator's Covid-19 Rapid-Response Dataset, Erin M. Buchanan, Andree Hartanto

Research Collection School of Social Sciences

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with …

Data Quality Checks: Implementation With Popular Data Collection Crowdsourcing Platforms, James Down, Gregory Balkcom, Kristine Duncan, Ngan (An) Truong, Andrew Lewis

Symposium of Student Scholars

The utilization of online crowdsourcing platforms for data collection has increased over the past two decades in the field of public health due to the ease of use, the cost-saving benefits, the speed of the data collection process, and the accessibility of a potentially true representative population. Although these platforms offer many advantages to researchers, significant drawbacks exist, such as poor data quality, that threaten the reliability and validity of the study. Previous studies have examined data quality concerns, but differences in results arise due to variations in study designs, disciplinary contexts, and the platforms being investigated. Therefore, this study …

Surveillance Systems In Western Kenya: Methods, Perceptions, And Effectiveness, Marissa Duffy

Independent Study Project (ISP) Collection

Surveillance is an important tool in monitoring and evaluating infectious disease patterns and trends. Surveillance is vital because it aids public health officials and medical professionals in creating better prevention methods and efficiently managing outbreaks. Kenya is home to many noncommunicable diseases making it an important location to conduct disease surveillance. Within Kenya, each county has its own surveillance unit which tracks and controls outbreaks. In addition, government run surveillance systems were established to determine disease burden, incidence, and patterns in specific at-risk communities around Kenya. One of these major surveillance systems is Population-Based Infectious Disease Surveillance (PBIDS) which has …

Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross

Masters Theses

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.

In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …

Evidence Assisted Learning For Clinical Decision Support Systems, Bhanu Pratap Singh Rawat

Doctoral Dissertations

Clinical decision support systems (CDSS) provide intelligently filtered knowledge and patient-specific and population information to the clinicians, nursing staff and healthcare professionals. CDSS can significantly improve the quality, safety, efficiency and effectiveness of health care. Over the last decade, American hospitals have adopted electronic health records (EHRs) widely resulting in a massive collection of clinical notes such as admission notes, physician notes, nursing notes and discharge summaries. For the past couple of decades, most of the work in CDSS has been focused on developing knowledge-based systems using structured data such as medications and ICD codes. In contrast, the EHR notes …

Analytical Approach For Monitoring The Behavior Of Patients With Pancreatic Adenocarcinoma At Different Stages As A Function Of Time, Aditya Chakaborty Dr, Chris P. Tsokos Dr

Biology and Medicine Through Mathematics Conference

No abstract provided.

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

Open Data Indicates That Collegedale Could Be A Bluezone, Tristan Deschamps, Alva Johnson

Campus Research Day

A blue zone is an indicator of exceptional health in a community. Adventists have a blue zone community in Loma Linda, but there has been little research into other Adventist populated areas that could be blue zones. Therefore, our goal is to show that open data suggests that a blue zone may exist near Southern Adventist University, specifically in Collegedale. This data has been gathered from different federal sources, including, the CDC, the US Census Bureau, the Tennessee Department of Health, official state records, and federal documents that are available to the public.

Predicting Micronutrient Deficiency With Publicly Available Satellite Data, Elizabeth Bondi-Kelly, Haipeng Chen, Christopher D. Golden, Nikhil Behari, Milind Tambe

Arts & Sciences Articles

Micronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin …

Determining The Proportionality Of Ischemic Stroke Risk Factors To Age, Elizabeth Hunter, John D. Kelleher

Articles

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over-or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, eg, risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data …

Prevention Research Center For Healthy Neighborhoods, Madeline Panus

Celebration of Scholarship 2023

No abstract provided.

The Daily Patterns Of Emergency Medical Events, Mary E. Helander, Margaret K. Formica, Dessa K. Bergen-Cico

Social Science - All Scholarship

This study examines population level daily patterns of time-stamped emergency medical service (EMS) dispatches to establish their situational predictability. Using visualization, sinusoidal regression, and statistical tests to compare empirical cumulative distributions, we analyzed 311,848,450 emergency medical call records from the U.S. National Emergency Medical Services Information System (NEMSIS) for years 2010 through 2022. The analysis revealed a robust daily pattern in the hourly distribution of distress calls across 33 major categories of medical emergency dispatch types. Sinusoidal regression coefficients for all types were statistically significant, mostly at the p < 0.0001 level. The coefficient of determination ($R^2$) ranged from 0.84 and 0.99 for all models, with most falling in the 0.94 to 0.99 range. The common sinusoidal pattern, peaking in mid-afternoon, demonstrates that all major categories of medical emergency dispatch types appear to be influenced by an underlying daily rhythm that is aligned with daylight hours and common sleep/wake cycles. A comparison of results with previous landmark studies revealed new and contrasting EMS patterns for several long-established peak occurrence hours--specifically for chest pain, heart problems, stroke, convulsions and seizures, and sudden cardiac arrest/death. Upon closer examination, we also found that heart attacks, diagnosed by paramedics in the field via 12-lead cardiac monitoring, followed the identified common daily pattern of a mid-afternoon peak, departing from prior generally accepted morning tendencies. Extended analysis revealed that the normative pattern prevailed across the NEMSIS data when re-organized to consider monthly, seasonal, daylight-savings vs civil time, and pre-/post- COVID-19 periods. The predictable daily EMS patterns provide impetus for more research that links daily variation with causal risk and protective factors. Our methods are straightforward and presented with detail to provide accessible and replicable implementation for researchers and practitioners.

Lessons Learned From Interdisciplinary Efforts To Combat Covid-19 Misinformation: Development Of Agile Integrative Methods From Behavioral Science, Data Science, And Implementation Science, Sahiti Myneni, Paula Cuccaro, Sarah Montgomery, Vivek Pakanati, Jinni Tang, Tavleen Singh, Olivia Dominguez, Trevor Cohen, Belinda Reininger, Lara S Savas, Maria E Fernandez

Journal Articles

BACKGROUND: Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals' preventive behaviors, including masking, testing, and vaccine uptake.

OBJECTIVE: In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation.

METHODS: We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Estimating The Health Effects Of Adding Bicycle And Pedestrian Paths At The Census Tract Level: Multiple Model Comparison, Ross J. Gore, Christopher Lynch, Craig Jordan, Andrew Collins, R. Michael Robinson, Gabrielle Fuller, Pearson Ames, Prateek Keerthi, Yash Kandukuri

VMASC Publications

Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions.

Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” …

Ingredient Classification Using Food Ontology, Ricky Flores

UNO Student Research and Creative Activity Fair

A food label provides some of the most crucial information for a food product. The food label is a key resource for many health-conscious consumers for understanding ingredients. It is also vital for individuals to avoid food allergens or help patients follow dietary recommendations. While the food labels in the United States are regulated by the Food and Drug Administration (FDA) many labels contain additional information or statements that are not regulated. Moreover, the food label may be complex or contain terminology that the layperson may not understand. Evidence has indicated that consumers often find nutrition labels confusing, especially when …

Estimating Weighted Panel Sizes For Primary Care Providers: An Assessment Of Clustering And Novel Methods Of Panel Size Estimation On Electronic Medical Records, Martin A. Lavallee

Theses and Dissertations

Primary Care is on the frontlines of healthcare, thus they see the most diverse set of patients. In order to achieve high functioning primary care, a practice must establish empanelment, the pairing of patients to providers. Enumeration of empanelment, or estimating panel sizes, helps ensure that the demands of the patients demand the supply of providers and optimize the balance of primary care resources to improve quality of care. Further we can adjust panel sizes by using patient-level data on healthcare utilization and complexity extracted from the electronic medial record to determine the amount of care or burden of work …

Addressing Ascertainment Bias In The Study Of Cardiovascular Disease Burden In Opioid Use Disorders - Application Of Natural Language Processing Of Electronic Health Records, Jade Huang Singleton

Theses and Dissertations--Epidemiology and Biostatistics

In the United States, the prevalence of long-term exposure to opioid drugs, for both medically and nonmedically indicated purposes, has increased considerably since the mid-1990’s. Concerns have emerged about the potential health effects of opioid use. There is also growing interest in other possible connections with opioid use including cardiovascular disease. Electronic health records (EHR) contain information about patient care in the form of structured codes and unstructured notes. Natural language processing (NLP) provides a tool for processing unstructured textual data in EHR clinical notes and extracts useful information for research with structured formats. The purpose of this dissertation was …

High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki

Doctoral Dissertations

Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …

Anti-Vaxxers: Parents Fighting Science, Katie West

Symposium of Student Scholars

Immunizing children helps protect the health of our community, especially those people who cannot be immunized. Yet, since 1996 after a study was released that linked autism to vaccinations, there has been a trend of parents refusing to vaccinate their children. What are the demographics of the parents who believe their children are better off without vaccines? By knowing where these parents live and what decisions they make for their children’s education, counties and medical professionals can provide education and address their concerns.

My research involves data on 116,141 kindergarten classes from 2000-2015 in California. The two vaccine exemption options …

Opioid Abuse: Are Doctors Creating The Problem?, Nguyen Tran

Symposium of Student Scholars

Opioid abuse and overdose are serious health problems in the United States. Current research has concentrated on the treatment and prevention of opioid abuse. Using data from the Controlled Substance Utilization Review and Evaluation System (CURES) for California zip codes, my research focuses on the causes of opioid overdose by considering the relationships between the following variables within each zip code: population size, average number of prescriptions per doctor, percentage of people who receive opioid prescriptions, percentage of people receiving the same prescription drug from 3 or more doctors, average number of opioid pills per prescription and number of people …

Food Deserts: Hungry For Answers, Lawren Cumberbatch

Symposium of Student Scholars

In 2010, the United States Department of Agriculture (USDA) reported that 23.5 million people in the United States live in food deserts. As defined by the USDA, a “food desert” is a neighborhood that lacks healthy food sources. This can be measured by distance to a store, number of stores in an area, individual-level resources such as family income or vehicle availability, and neighborhood-level resources such as availability of public transportation. Past research provides evidence that food deserts are especially likely to occur in communities heavily populated by minorities. As a Black Indian pre-med student aiming to join the world …

Reporting Of Eating Disorder Deaths, Katherine Mobley, Amy Hord

Symposium of Student Scholars

Those affected by eating disorders experience disturbances in eating behaviors which are often related to underlying psychiatric disorders such as anxiety, depression, or obsessive-compulsive disorder (Parekh, 2017, Drieberg et al., 1998 p.53). The duplicitous nature of the disorder makes it difficult to diagnose, and the tole it takes on an individual’s physical health makes its mortality rate the second highest among psychiatric disorders (Guinhut et al., 2021 p.130). Even if the correct education and resources are accessible to certain individuals, negative stigmatization about the disorder can make sufferers unlikely to seek help (Becker et al., 2010). Findings from analysis of …

Predictive Modeling And Estimation Of The Doubling Time Of Confirmed Cases Of Covid-19 In Niger, Ibrahim Sidi Zakari, Hadiza Galadima

Community & Environmental Health Faculty Publications

Modeling is increasingly used to assess scenarios and make projections on the future course of new coronavirus disease. This allows for better planning of care as well as a relaxation or tightening of the restrictive measures decreed by the government and the health authorities. The data analyzed in this study covers the period from March 19 to June 05, 2020 and allowed predictions of new cases of COVID-19 based on a growth model with a growth rate that changes linearly over time. In addition, we calculated and predicted the doubling time of the number of positive cases in each region …

Sars-Cov-2 Pandemic Analytical Overview With Machine Learning Predictability, Anthony Tanaydin, Jingchen Liang, Daniel W. Engels

SMU Data Science Review

Understanding diagnostic tests and examining important features of novel coronavirus (COVID-19) infection are essential steps for controlling the current pandemic of 2020. In this paper, we study the relationship between clinical diagnosis and analytical features of patient blood panels from the US, Mexico, and Brazil. Our analysis confirms that among adults, the risk of severe illness from COVID-19 increases with pre-existing conditions such as diabetes and immunosuppression. Although more than eight months into pandemic, more data have become available to indicate that more young adults were getting infected. In addition, we expand on the definition of COVID-19 test and discuss …

Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth

Publications

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …

Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes, Shravani Chitineni, Elizabeth R. Gleim, Holly D. Gaff

Undergraduate Honors Theses

Annual Lyme disease cases continue to rise in the U.S. making it the most reported vector-borne illness in the country. The pathogen (Borrelia burgdorferi) and primary vector (Ixodes scapularis; blacklegged tick) dynamics of Lyme disease are complicated by the multitude of vertebrate hosts and varying environmental factors, making models an ideal tool for exploring disease dynamics in a time- and cost-effective way. In the current study, LYMESIM 2.0, a mechanistic model, was used to explore the effectiveness of three commonly used tick control methods: habitat-targeted acaricide (spraying), rodent-targeted acaricide (bait boxes), and white-tailed deer targeted acaricide (4-poster …

Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …