Rob Schumaker

Rob Schumaker
The University of Texas at Tyler · Department of Computer Science

PhD

About

61
Publications
51,051
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2,336
Citations
Introduction
Skills and Expertise

Publications

Publications (61)
Article
Full-text available
Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was 75.5% accurate. Model 2 used patient prescriptions and was 64.9% accurate. Model 3 used both patient...
Preprint
Full-text available
The opioid epidemic is taxing medical infrastructure in terms of patient care and costs. Being able to prioritize care based on expected outcomes could improve patient safety and reduce costs. To investigate, we constructed a decision tree to examine what opioids in conjunction with heroin were most likely to lead to patient death versus their abil...
Chapter
Full-text available
This book covers key areas of Pharmaceutical Research and Development. The contributions by the authors include subaerial algae, antioxidant, flavonoids, free radicals, superoxide anions, carotenoids, inhibition concentration, column chromatography, Biosynthesis, gold nanoparticles, antimicrobial activity, anticancer activity, adverse drug event, f...
Article
Full-text available
Health care delivery revolves around accurate documentation, with data management free from error. A seemingly insignificant typographical error can cause short- and long-term problems that may lead to inaccurate records and misinformation. This report presents an overview of input errors in the US Food and Drug Administration Adverse Event Reporti...
Preprint
UNSTRUCTURED We use a data driven approach on a cleaned FAERS database to determine the adverse drug reaction severity of several covid-19 drug combinations and further investigate their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine is associated with increased a...
Article
Full-text available
Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan th...
Article
Most modern machine learning research is devoted to improving the accuracy of prediction. However, less attention is paid to the deployment of the machine and deep learning systems, supervised/unsupervised techniques for mining healthcare data, and time series similarity and irregular temporal data analysis [item 1)–9) in the Appendix]. Most deploy...
Article
Full-text available
Background: The U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), contains information on adverse drug events and medication error reports submitted to the FDA through the MedWatch program. A significant number of adverse events reported in the FAERS database have been for opioid use. The objective of this study was to dete...
Article
To predict NFL game outcomes, we examine the application of technical stock market techniques to sentiment gathered from social media. From our analysis we found a $14.84 average return per sentiment-based wager compared to a $12.21 average return loss on the entire 256 games of the 2015–2016 regular season if using an odds-only approach. We furthe...
Article
Full-text available
Can the sentiment contained in tweets serve as a meaningful proxy to predict match outcomes and if so, can the magnitude of outcomes be predicted based on a degree of sentiment? To answer these questions we constructed the CentralSport system to gather tweets related to the twenty clubs of the English Premier League and analyze their sentiment cont...
Chapter
The purpose of this research is to demonstrate the efficiency of the Electronic Health Record (EHR) software that is adopted in the healthcare industry to provide better patient care. The authors examine the impact of EHRs on the efficient delivery of healthcare services. More specifically, they detail the origin of EHR, its significance in modern...
Article
Full-text available
The DIKW hierarchy has long been a standard framework with which researchers can differentiate between levels of what they see and know. However much of the research conducted explores the nuances and precise divisions between each hierarchy level and assumes that the user will know how to use them. We plan to restrict our study to textual Web docu...
Article
The purpose of this research is to demonstrate the efficiency of the Electronic Health Record (EHR) software that is adopted in the healthcare industry to provide better patient care. The authors examine the impact of EHRs on the efficient delivery of healthcare services. More specifically, they detail the origin of EHR, its significance in modern...
Article
Racing prediction schemes have been with mankind a long time. From following crowd wisdom and betting on favorites to mathematical methods like the Dr. Z System, we introduce a different class of prediction system, the S&C Racing system that derives from machine learning. We demonstrate the S&C Racing system using Support Vector Regression (SVR) to...
Article
Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it...
Chapter
However, using computational approaches to predict stock prices using financial data is not unique. In recent years, interest has increased in Quantitative funds, or Quants, that automatically sift through numeric financial data and issue stock recommendations. While these systems are based on proprietary technology, they do differ in the amount of...
Chapter
This chapter investigates the role that statistics plays in knowledge creation. While many of these techniques have stood the test of time, some have undergone intense scrutiny while others have experienced transformative processes. All the while we must ask ourselves, are we really measuring what we think we are measuring? This chapter will help t...
Article
Full-text available
This work investigates the possibility of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText). In particular we compare AZFinText's predictions against existing quantitative funds using textual representation and statistical machine learning me...
Article
Incredible amounts of data exist across all domains of sports. This data can come in the form of individual player performance, coaching or managerial decisions, game-based events and/or how well the team functions together. The task is not how to collect the data, but what data should be collected and how to make the best use of it. By finding the...
Article
Full-text available
Sports information and footage are quickly becoming increasingly available in digital form. Using many of the tools previously outfitted for textual searching, video and multimedia searching and retrieval is becoming more commonplace in sports. Automated methods to watch and listen to games are being used to parse video and render it in searchable...
Article
How is it that we value data? Is a simple repository of data all that we need? It used to be that carrying a copy of Total Baseball was all that was ever needed, as it provided a historical perspective of player data that was adequate for our needs only a decade ago. Then as sabermetrics began to awaken the sporting world’s desire for more data and...
Article
Data, the life-blood of modern sport analysis, has undergone its own revolution. It used to be that data was simply viewed as a record of the game’s events that was kept either by the organizations or the responsible leagues for historical purposes. That data became transformed into a condensed form to provide a brief recap of the game’s events thr...
Article
Uncertainty is inevitable in problem solving and decision making. One way to reduce it is by seeking the advice of an expert. When we use computers to reduce uncertainty, the computer itself can become an “expert” in a specific field through a variety of methods. One such method is machine learning, which involves using a computer algorithm to capt...
Article
Full-text available
Open source development has become more prominent in recent years in a multitude of software areas. In the domain of data mining tools, several solutions have gained significant acceptance such as Weka and RapidMiner. Both tools share the same underlying learning algorithms, however, their approach to displaying results, are very much different.
Article
Data Mining involves procedures for uncovering hidden trends and developing new data and information from data sources. These sources can include well-structured and defined databases, such as statistical compilations, or unstructured data in the form of multimedia sources such as video broadcasts and play-by-play narration.
Article
In this chapter we investigate the role of machine learning within the domain of Greyhound Racing. We test a Support Vector Regression (SVR) algorithm on 1,953 races across 31 different dog tracks and explore the role of a simple betting engine on a wide range of wager types.
Article
This chapter investigates some of the data mining and scouting tools available for sports analysis. In particular, we analyze the role of these tools and how they can help an organization. Tools such as Advanced Scout, which maintains play-by-play data in an easy to query environment and Inside Edge, which provides pictorial descriptions of player...
Chapter
Predictive modeling has long been the goal of many individuals and organizations. This science has many techniques, with simulation and machine learning at its heart. Simulations such as basketball’s BBall can model an entire season and can deduce optimal substitution patterns and scoring potential of players. Should unforeseen events occur such as...
Article
Full-text available
This paper analyzes and compares the data gathered from two previously conducted artificial linguistic Internet chat entity (ALICE) chatterbot studies that were focused on response accuracy and user satisfaction measures for six chatterbots. These chatterbots were further loaded with varying degrees of conversational, telecommunications, and terror...
Article
Word Count: 17,721 2 Introduction Vast amounts of sports data are routinely collected about players, coaching decisions and game events. Making sense of this data is important to those seeking an edge. By transforming this data into actionable knowledge, scouts, managers and coaches can have a better idea of what to expect from opponents and be abl...
Article
Full-text available
Financial articles can move stock prices. The terms used in an article can be a predictor of both price direction and the magnitude of movement. By investigating the usage of terms in financial news articles and coupling them with a discrete machine-learning algorithm, we can build a model of short-term price movement. From our research, we investi...
Article
Full-text available
Article terms can move stock prices. By ana- lyzing verbs in financial news articles and coupling their usage with a discrete machine learning algorithm tied to stock price move- ment, we can build a model of price move- ment based upon the verbs used, to not only identify those terms that can move a stock price the most, but also whether they move...
Chapter
Over the next several years, sports data mining practices will be faced with several challenges and obstacles. The most obvious of which is to overcome the years of resistance by the members of sporting organizations that would rather stick with a traditional way of doing things. Aside from the challenges that are faced, sports data mining currentl...
Book
Data mining is the process of extracting hidden patterns from data, and its commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world ove...
Article
We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText).The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pr...
Article
Full-text available
Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: Bag of Words, Noun Phrases, and Named Entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the S&P 500 stocks during a five week period. We...
Article
Full-text available
We investigated the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentime...
Article
We study the coupling of basic quantitative portfolio selection strategies with a financial news article prediction system, AZFinText. By varying the degrees of portfolio formation time, we found that a hybrid system using both quantitative strategy and a full set of financial news articles, performed the best. With a 1-week portfolio formation per...
Article
Full-text available
In this paper we investigate the role of machine learning within the domain of Greyhound Racing. We test a Support Vector Regression (SVR) algorithm on 1,953 races across 31 different dog tracks and explore the role of a simple betting engine on a wide range of wager types. From this we triangulated our results on three dimensions of evaluation: ac...
Article
This paper investigates the potential use of dialog-based ALICEbots in disseminating terrorism information to the general public. In particular, we study the acceptance and response satisfaction of ALICEbot responses in both the general conversation and terrorism domains. From our analysis of three different knowledge sets: general conversation, te...
Article
Full-text available
The TARA Project examined how a trio of modified chatterbots could be used to disseminate terrorism-related information to the general public.
Article
An effective networked knowledge delivery platform is one of the Holy Grails of Web computing. Knowledge delivery approaches range from the heavy and narrow to the light and broad. This paper explores a lightweight and flexible dialog framework based on the ALICE system, and evaluates its performance in chat and knowledge delivery using both a conv...
Article
In this paper, we evaluate mass knowledge acquisition using modified ALICE chatterbots. In particular we investigate the potential of allowing subjects to modify chatterbot responses to see if distributed learning from a web environment can succeed. This experiment looks at dividing knowledge into general conversation and domain specific categories...
Conference Paper
Full-text available
This paper examines the role of financial news articles on three different textual representations; Bag of Words, Noun Phrases, and Named Entities and their ability to predict discrete number stock prices twenty minutes after an article release. Using a Support Vector Machine (SVM) derivative, we show that our model had a statistically significant...
Conference Paper
Full-text available
Terrorism research has fast become one of the more important fields in research of late. Researchers need to be able to quickly make connections from vast storehouses of publicly available resources. The problem of information overload rapidly becomes one of the central challenges to existing information retrieval systems. Commercial search engines...
Conference Paper
Terrorism research has lately become a national priority. Researchers and citizens alike are coming to grips with obtaining relevant and pertinent information from vast storehouses of information gateways, dictionaries, self-authoritative websites, and sometimes obscure government information. Specific queries need to be manually sought after, or l...
Conference Paper
Full-text available
Homeland security researchers and analysts more than ever must process large volumes of textual information. Information extraction techniques have been proposed to help alleviate the burden of information overload. Information extraction techniques, however, require retraining and/or knowledge re-engineering when document types vary as in the home...
Conference Paper
Full-text available
Ever since the 9-11 incident, the multidisciplinary field of terrorism has experienced tremendous growth. As the domain has benefited greatly from recent advances in information technologies, more complex and challeng- ing new issues have emerged from numerous counter-terrorism-related research communities as well as governments of all levels. In t...
Article
How is it that sports and gambling co-exist so easily together, yet can cause so many problems? We explore the relationship between sports and gambling from a historical perspective and describe the ways that some organizations are trying to keep a safe distance between the two.

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