Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, 2020 Washington University in St. Louis
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim
Engineering and Applied Science Theses & Dissertations
Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is ...
Gait Characterization Using Computer Vision Video Analysis, 2020 College of William and Mary
Gait Characterization Using Computer Vision Video Analysis, Martha T. Gizaw
Undergraduate Honors Theses
The World Health Organization reports that falls are the second-leading cause of accidental death among senior adults around the world. Currently, a research team at William & Mary’s Department of Kinesiology & Health Sciences attempts to recognize and correct aging-related factors that can result in falling. To meet this goal, the members of that team videotape walking tests to examine individual gait parameters of older subjects. However, they undergo a slow, laborious process of analyzing video frame by video frame to obtain such parameters. This project uses computer vision software to reconstruct walking models from residents of an independent living retirement ...
Rdc Data Alternatives: Conducting Research During Covid-19, 2020 Western University
Rdc Data Alternatives: Conducting Research During Covid-19, Kristi Thompson, Elizabeth Hill
Western Libraries Presentations
Recent physical distancing protocols pertaining to the COVID-19 Pandemic have meant that RDC researchers need to find alternatives ways of carrying out their research. The Real Time Remote Access (RTRA) program offers one alternative way to access confidential Statistics Canada data. Other options include using the Statistics Canada public use files and analyzing data from other sources.
The presenters, data librarians from Western Libraries will discuss the differences between the data that can be accessed through the RTRA the RDC. RTRA data is a very useful option for some types of questions but also has some important limitations. We will ...
Forecasting San Francisco Bay Area Rapid Transit (Bart) Ridership, 2020 Southern Methodist University (SMU)
Forecasting San Francisco Bay Area Rapid Transit (Bart) Ridership, Swee K. Chew, Alec Lepe, Aaron Tomkins, Peter Scheirer
SMU Data Science Review
In this paper, we present a forecasting analysis of the San Francisco Bay Area Rapid Transit (BART) ridership data utilizing a number of different time series methods. BART is a major public transportation system in the Bay Area and it relies heavily on its riders' fares; having models that generate accurate ridership numbers better enables the agency to project revenue and help manage future expenses. For our time series modeling, we utilized autoregressive integrated moving average (ARIMA), deep neural networks (DNN), state space models, and long short-term memory (LSTM) to predict monthly ridership. As there is such a wide range ...
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, 2020 Southern Methodist University
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied ...
Demand Forecasting For Alcoholic Beverage Distribution, 2020 Southern Methodist University
Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler
SMU Data Science Review
Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable resource to a company. While difficult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting ...
Home Sales As A Time Series Model, 2020 The University of Akron
Home Sales As A Time Series Model, Noah R. Hellenthal
Williams Honors College, Honors Research Projects
Rational Expectations Hypothesis is an economic theorem that states that our best way to predict the future is by looking at the past. While this theory is typically used to address inflation, the same concept can be used when predicting future home sales. With the failure of subprime mortgages and the burst of the housing market bubble in 2008, home sales are proven to be an appropriate indication of how the U.S. economy is performing. Through time series analysis, I will be able to construct a model with monthly home sales data from the U.S. Census Bureau. Due ...
Phenotype Extraction: Estimation And Biometrical Genetic Analysis Of Individual Dynamics, 2020 Virginia Commonwealth University
Phenotype Extraction: Estimation And Biometrical Genetic Analysis Of Individual Dynamics, Kevin L. Mckee
Theses and Dissertations
Within-person data can exhibit a virtually limitless variety of statistical patterns, but it can be difficult to distinguish meaningful features from statistical artifacts. Studies of complex traits have previously used genetic signals like twin-based heritability to distinguish between the two. This dissertation is a collection of studies applying state-space modeling to conceptualize and estimate novel phenotypic constructs for use in psychiatric research and further biometrical genetic analysis. The aims are to: (1) relate control theoretic concepts to health-related phenotypes; (2) design statistical models that formally define those phenotypes; (3) estimate individual phenotypic values from time series data; (4) consider hierarchical ...
Seasonal Time Series Models With Application To Weather And Lake Level Data, 2019 Missouri State University
Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin
MSU Graduate Theses
This work studies seasonal time series models with application to lake level and weather data. The thesis includes related time series concepts, integrated autoregressive moving average models (abbreviated as ARIMA), parameter estimation, model diagnostics, and forecasting. The studied time series models are applied to the data of daily lake level in Beaver Lake (1988-2017) and the data of daily maximum temperature in New York Central Park (1870-2017). Due to seasonality of the data, three diﬀerent approaches are proposed to the modeling: regression method, functional ARIMA method and multiplicative seasonal ARIMA method. The forecasted values of the year 2018 are compared ...
What Have Long-Term Field Studies Taught Us About Population Dynamics?, 2019 Pennsylvania State University
What Have Long-Term Field Studies Taught Us About Population Dynamics?, Beth A. Reinke, David A. W. Miller, Fredric J. Janzen
Ecology, Evolution and Organismal Biology Publications
Long-term studies have been crucial to the advancement of population biology, especially our understanding of population dynamics. We argue that this progress arises from three key characteristics of long-term research. First, long-term data are necessary to observe the heterogeneity that drives most population processes. Second, long-term studies often inherently lead to novel insights. Finally, long-term field studies can serve as model systems for population biology, allowing for theory and methods to be tested under well-characterized conditions. We illustrate these ideas in three long-term field systems that have made outsized contributions to our understanding of population ecology, evolution, and conservation biology ...
Long-Run Impacts Of Trade Shocks And Export Competitiveness: Evidence From The U.S. Bse Event, 2019 Iowa State University
Long-Run Impacts Of Trade Shocks And Export Competitiveness: Evidence From The U.S. Bse Event, Chen-Ti Chen, John M. Crespi, William Hahn, Lee L. Schulz, Fawzi Taha
CARD Working Papers
This paper examines how comparative advantages of major beef exporters changed following the 2003 bovine spongiform encephalopathy outbreak (BSE), which significantly disrupted the U.S. beef trade until approximately April 2007. Using longitudinal data on beef export values and constructed revealed comparative advantage measures, we show that while some measure of the long-run impacts of BSE on U.S. beef export competitiveness have returned to pre-2003 levels, the U.S.’s comparative advantage has not. We also examine a hypothetical scenario of no BSE event in 2003 and predict what exporters’ competitiveness would have looked like. The authors discuss the ...
Predicting Wind Turbine Blade Erosion Using Machine Learning, 2019 Southern Methodist University
Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta
SMU Data Science Review
Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity ...
Longitudinal Analysis With Modes Of Operation For Aes, 2019 Southern Methodist University
Longitudinal Analysis With Modes Of Operation For Aes, Dana Geislinger, Cory Thigpen, Daniel W. Engels
SMU Data Science Review
In this paper, we present an empirical evaluation of the randomness of the ciphertext blocks generated by the Advanced Encryption Standard (AES) cipher in Counter (CTR) mode and in Cipher Block Chaining (CBC) mode. Vulnerabilities have been found in the AES cipher that may lead to a reduction in the randomness of the generated ciphertext blocks that can result in a practical attack on the cipher. We evaluate the randomness of the AES ciphertext using the standard key length and NIST randomness tests. We evaluate the randomness through a longitudinal analysis on 200 billion ciphertext blocks using logistic regression and ...
Sample Size Calculation Of Clinical Trials With Correlated Outcomes, 2019 Southern Methodist University
Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li
Statistical Science Theses and Dissertations
In this thesis, we investigate sample size calculation for three kinds of clinical trials: (1). Randomized controlled trials (RCTs) with longitudinal count outcomes; (2). Cluster randomized trials (CRTs) with count outcomes; (3). CRTs with multiple binary co-primary endpoints.
Estimation Of Association Between A Longitudinal Marker And Interval-Censored Progression Times, 2019 Portland State University
Estimation Of Association Between A Longitudinal Marker And Interval-Censored Progression Times, Naghmeh Daneshi
Dissertations and Theses
In longitudinal studies, we observe the subjects who are likely to progress to a new state during the study time. For example, in clinical trials the stage of a progressing disease is recorded at each follow-up visit. The primary goal is to estimate the relationship between the attributes and the subject's progression state. In such studies, some subjects complete all their follow-up visits and their progression state are observed without any missingness. However, others miss their follow-up visits and when they come back, they learn that they have progressed to a new state. In this case, not only are ...
Status Of The Topeka Shiner In Iowa, 2019 U.S. Geological Survey
Status Of The Topeka Shiner In Iowa, Clay L. Pierce, Nicholas T. Simpson, Alexander P. Bybel, Courtney L. Zambory, Michael J. Weber, Kevin J. Roe
Natural Resource Ecology and Management Publications
The Topeka shiner Notropis topeka is native to Iowa, Kansas, Minnesota, Missouri, Nebraska, and South Dakota and has been federally listed as endangered since 1998. Our goals were to determine the present distribution and qualitative status of Topeka shiners throughout its current range in Iowa and characterize the extent of decline in relation to its historic distribution. We compared the current (2016–2017) distribution to distributions portrayed in three earlier time periods. In 2016–2017 Topeka shiners were found in 12 of 20 HUC10 watersheds where they occurred historically. Their status was classified as stable in 21% of the HUC10 ...
Multivariate Temporal Modeling Of Crime With Dynamic Linear Models, 2019 Iowa State University
Multivariate Temporal Modeling Of Crime With Dynamic Linear Models, Nathaniel Garton, Jarad Niemi
Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through ...
Copula-Based Zero-Inflated Count Time Series Models, 2019 Old Dominion University
Copula-Based Zero-Inflated Count Time Series Models, Mohammed Sulaiman Alqawba
Mathematics & Statistics Theses & Dissertations
Count time series data are observed in several applied disciplines such as in environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts if they are recorded over time. Overlooking the frequent occurrence of zeros and the serial dependence could lead to false inference. In this dissertation, we propose two classes of copula-based time series models for zero-inflated counts with the presence of covariates. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals of the ...
Field Drilling Data Cleaning And Preparation For Data Analytics Applications, 2019 Louisiana State University
Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga
LSU Master's Theses
Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different ...
Identification Of Treatment Effects With Mismeasured Imperfect Instruments, 2019 Iowa State University
Identification Of Treatment Effects With Mismeasured Imperfect Instruments, Desire Kedagni
Economics Working Papers
In this article, I develop a novel identification result for estimating the effect of an endogenous treatment using a proxy of an unobserved imperfect instrument. I show that the potential outcomes distributions are partially identified for the compliers. Therefore, I derive sharp bounds on the local average treatment effect. I write the identified set in the form of conditional moments inequalities, which can be implemented using existing inferential methods. I illustrate my methodology on the National Longitudinal Survey of Youth 1979 to evaluate the returns to college attendance using tuition as a proxy of the true cost of going to ...