Operations Research, Systems Engineering and Industrial Engineering Commons^™

Mining High Impact Combinations Of Conditions From The Medical Expenditure Panel Survey, Arjun Mohan

Masters Theses

The condition of multimorbidity — the presence of two or more medical conditions in an individual — is a growing phenomenon worldwide. In the United States, multimorbid patients represent more than a third of the population and the trend is steadily increasing in an already aging population. There is thus a pressing need to understand the patterns in which multimorbidity occurs, and to better understand the nature of the care that is required to be provided to such patients.

In this thesis, we use data from the Medical Expenditure Panel Survey (MEPS) from the years 2011 to 2015 to identify …

Automated Sentiment Analysis For Personnel Survey Data In The Us Air Force Context, Julia M. Haines

Theses and Dissertations

When surveys are distributed across the Air Force (AF), whether it be an employee engagement survey, a climate survey, or similar, significant resources are put towards the development, distribution and analysis of the survey. However, when open ended questions are included on these surveys, respondent comments are generally underutilized, more often treated as a source for pull-quotes rather than a data source in and of themselves. This is due to a lack of transparency and confidence in the accuracy of machine-aided methods such as sentiment analysis and topic modeling. This confidence reduces further when the text has special context, such …

A Two-Stage Approach To Ridesharing Assignment And Auction In A Crowdsourcing Collaborative Transportation Platform., Peiyu Luo

Electronic Theses and Dissertations

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open …

Comparación De Cuatro Métodos De Predicción Para Dos Acciones En La Bolsa De Valores De Colombia, María Angélica Rey Vesga, Juan Alfonso Chamorro Chamorro

Ingeniería Industrial

Los mercados de valores, más exactamente en su sección de títulos de renta variable, representan una alternativa de inversión donde tanto las organizaciones como los inversionistas se ven beneficiados, sin embargo, debido a su alto grado de variabilidad, las inversiones en este mercado conllevan un alto riesgo. Por tanto, la cuestión radical en la posibilidad de perder parte o la totalidad de la inversión. Es en este punto, donde los pronósticos adquieren un alto nivel de relevancia y el generar proyecciones anticipadas del futuro basadas en información histórica representa una clara ventaja, genera mayor claridad del panorama e incrementa la …

Ensemble Machine Learning To Predict Family Consent For Organ Donation, Md Ehsan Khan

Graduate Dissertations and Theses

There is ever increasing disparity between number of organs needed for transplantation and numbers available for donation to save lives. As a result, thousands of people die every year waiting for organs. Therefore, it is now more important than ever before to take serious actions to decrease this disparity. One way to bridge gap between organ demand and supply is to increase family consent for organ donation. This research studied the factors associated with family consent. Machine Learning approach had been used in very few literature to understand factors related to family consent. This study uses six Ensemble Machine Learning …

An Open Source Approach To Social Media Data Gathering, Anthony J. Kallhoff

Theses and Dissertations

Modern usage of social media affords the military intelligence and analytic communities novel approaches to gather information. However, the tools and resources to develop these methodologies are still maturing. Furthermore, current data acquisition tools are not available to the DoD for all social media platforms. This thesis addresses a small subset of this problem by developing an open source methodological approach to collect and manage data from a popular social media site that has previously been inaccessible to defense intelligence organizations. This approach was operationalized via the R package called instaExtract, and an exemplar analysis was performed to demonstrate its …

The Application Of Artificial Neural Networks For Prioritization Of Independent Variables Of A Discrete Event Simulation Model In A Manufacturing Environment, Rebecca Pires Dos Santos

Theses and Dissertations

The high complexity existent in businesses has required managers to rely on accurate and up to date information. Over the years, many tools have been created to give support to decision makers, such as discrete event simulation and artificial neural networks. Both tools have been applied to improve business performance; however, most of the time they are used separately. This research aims to interpret artificial neural network models that are applied to the data generated by a simulation model and determine which inputs have the most impact on the output of a business. This would allow prioritization of the variables …

Statistical And Prognostic Modeling Of Clinical Outcomes With Complex Physiologic Data, Monica A. Puertas

USF Tampa Graduate Theses and Dissertations

Laboratory tests are a primary resource for diagnosing patient diseases. However, physicians often make decisions based on a single laboratory result and have a limited perspective of the role of commonly-measured parameters in enhancing the diagnostic process. By providing a dynamic patient profile, the diagnosis could be more accurate and timely, allowing physicians to anticipate changes in the recovery trajectory and intervene more effectively.

The assessment and monitoring of the circulatory system is essential for patients in intensive care units (ICU). One component of this system is the platelet count, which is used in assessing blood clotting. However, platelet counts …

An Unsupervised Consensus Control Chart Pattern Recognition Framework, Siavash Haghtalab

Electronic Theses and Dissertations

Early identification and detection of abnormal time series patterns is vital for a number of manufacturing. Slide shifts and alterations of time series patterns might be indicative of some anomaly in the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not …

A Stand-Alone Methodology For Data Exploration In Support Of Data Mining And Analytics, Michael Gage

Master's Theses

With the emergence of Big Data, data high in volume, variety, and velocity, new analysis techniques need to be developed to effectively use the data that is being collected. Knowledge discovery from databases is a larger methodology encompassing a process for gathering knowledge from that data. Analytics pair the knowledge with decision making to improve overall outcomes. Organizations have conclusive evidence that analytics provide competitive advantages and improve overall performance. This paper proposes a stand-alone methodology for data exploration. Data exploration is one part of the data mining process, used in knowledge discovery from databases and analytics. The goal of …

A Methodology For Scheduling Operating Rooms Under Uncertainty, Marbelly Paola Davila

USF Tampa Graduate Theses and Dissertations

An operating room (OR) is considered to be one of the most costly functional areas within hospitals as well as its major profit center. It is known that managing an OR department is a challenging task, which requires the integration of many actors (e.g., patients, surgeons, nurses, technicians) who may have conflicting interests and priorities.

Considering these aspects, this dissertation focuses on developing a simulation based methodology for scheduling operating rooms under uncertainty, which reflects the complexity, uncertainty and variability associated with surgery.

We split the process of scheduling ORs under uncertainty into two main components. First, we designed a …

Decision Rule Induction For Service Sector Using Data Mining- A Rough Set Theory Approach, Zhonghua Hu

Open Access Theses & Dissertations

Nowadays, data mining is more widely used than ever before; not only by the academic area, but also in the industry and business area. Apart from execution of business processes, the creation of knowledge base and its utilization for the benefit of the organization is becoming a strategy tool to compete. Despite of having ever growing data bases, the problem is that the finance company fails to fully capitalize the true benefits which can be gained from this great wealth of information. The data mining technology instead of classic statistical analysis is developed to help the people to discover the …

A Study Of Distributed Clustering Of Vector Time Series On The Grid By Task Farming, Arun B. Nayar

LSU Master's Theses

Traditional data mining methods were limited by availability of computing resources like network bandwidth, storage space and processing power. These algorithms were developed to work around this problem by looking at a small cross-section of the whole data available. However since a major chunk of the data is kept out, the predictions were generally inaccurate and missed out on significant features that was part of the data. Today with resources growing at almost the same pace as data, it is possible to rethink mining algorithms to work on distributed resources and essentially distributed data. Distributed data mining thus holds great …