Machine learning approaches for assessing moderate-to-severe diarrhea in children \u3c 5 years of age, rural western Kenya 2008-2012

Worldwide diarrheal disease is a leading cause of morbidity and mortality in children less than five years of age. Incidence and disease severity remain the highest in sub-Saharan Africa. Kenya has an estimated 400,000 severe diarrhea episodes and 9,500 diarrhea-related deaths per year in children. Current statistical methods for estimating etiological and exposure risk factors for moderate-to-severe diarrhea (MSD) in children are constrained by the inability to assess a large number of parameters due to limitations of sample size, complex relationships, correlated predictors, and model assumptions of linearity. This dissertation examines machine learning statistical methods to address weaknesses associated with using traditional logistic regression models. The studies presented here investigate data from a 4-year, prospective, matched case-control study of MSD among children less than five years of age in rural Kenya from the Global Enteric Multicenter Study. The three machine learning approaches were used to examine associations with MSD and include: least absolute shrinkage and selection operator, classification trees, and random forest. A principal finding in all three studies was that machine learning methodological approaches are useful and feasible to implement in epidemiological studies. All provided additional information and understanding of the data beyond using only logistic regression models. The results from all three machine learning approaches were supported by comparable logistic regression results indicating their usefulness as epidemiological tools. This dissertation offers an exploration of methodological alternatives that should be considered more frequently in diarrheal disease epidemiology, and in public health in general

A predictive model for MSSW student success.

This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with the subjective experiences of academic stability and academic performance were studied. A hierarchical multiple regression analysis was used to determine the best predictors of final GPA. The best predictors were age, undergraduate GPA, differences between undergraduate and graduate institution size, continuity index, and the course completion ratio. A hierarchical logistic regression analysis was used to determine the best predictors of graduation with an MSSW degree. The best predictors were age, prerequisite classes, rural/metropolitan nature of hometown, continuity index, course completion ratio and full-time student status in the first semester. Potential interventions and policy changes are detailed at both entry into and during the MSSW program. There is a need for future research in subsequent years at the Kent School of Social Work and other schools of social work that offer Master\u27s degrees

The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe prediction of academic success is a major topic in higher education, especially among the academic community. In this dissertation, we are going to present a data mining approach taking into consideration the features that are the most relevant in terms of successful academic achievement of the Bachelors’ programs at Nova School of Business and Economics (Nova SBE). Initially, we are going to perform a literature review in order to understand the framework of academic success and also to make a summary of previous research on the field of educational data mining when used to assess student success. Subsequently, the empirical approach will start being developed with the extraction of socio-economic, socio-demographic, and academic data of students, which will result in our main dataset. Later, and after the data discovery, data cleansing, and transformation activities, a set of features are going to be taken into consideration according to their relevance for the subject. Based on the dataset containing these features, several predictive data-driven techniques are going to be applied, resulting in models which are going to be assessed in order to understand if the selected features are relevant enough to answer our problem or if there is a need to substitute them by other attributes. This process will result in several iterations that will confer credibility and robustness to the model that demonstrates the best performance in classifying students’ academic success. In the end, it is intended that the insights extracted from the model will provide the school key stakeholders with enough knowledge to capacitate them to take actions that will result in the maximization of the students learning success

What Factors Influence Affirmative-Action Students\u27 Achievement in Brazilian Federal Universities?

This study aims to determine what personal and family characteristics, pre-college factors and environmental variables, contribute to affirmative action student success in Brazilian federal universities, as measured by the final college exam. In 2012, Brazil implemented an aggressive and controversial quota-system in federal universities which reserved half of the incoming class spaces to students who graduated from public high schools, followed by prioritization based on income and race. The study used secondary data collected by the Brazilian Ministry of Education; the population includes 6,557 graduating students from the sampled federal universities majors who completed the 2016 ENADE exam (final college exam). Using the IBM statistical package for the social sciences (SPSS), Statistics 24 was utilized to conduct hierarchical multiple regressions analyses. Hierarchical multiple regressions were run to determine if the addition of high school factors, pre-college variables, economic factors, social support variables, and individual involvement variables obtained from a survey improved the prediction of ENADE (college exit exam) scores over and above the family and student characteristic variables alone. The full model, inclusive of family income, age, sex, race, father’s educational level, mother’s educational level, high school location, traditional vs. non-traditional high school, public or private high school, reason for choosing major, university proximity to home, evening student, on-campus living, SES or disability quota type, STEM vs. Non-STEM majors, highly selective university, awarded scholarship, academic involvement scholarship, contributed to family finances, retention services financial need, working student, institutional and faculty support, received support while facing challenges, general social support to attend college, reading for pleasure, opportunity to learn a foreign language and number of hours spent studying – to predict ENADE overall score was statistically significant, R2=0.104, F(3,6529)= 50.915, p2=0.10. This study adds to prior research by extending U.S.-based student development theory, specifically Astin’s theory of student involvement to a new population, Brazilian university students. It also focuses on the academic achievement of quota-students only, giving legitimized quota-students in higher education, a space in which they have been habitually excluded and marginalized. Quota-students are primarily analyzed in the context of measuring up to the academic level of non-quota students

Neglected Cultural Outcomes That Impact Hispanic-Serving Institution Policymaking

The Higher Education Act (HEA) Title V is designed to expand opportunities, increase attainment, and enhance institutional quality and stability of Hispanic-Serving Institutions (HSIs). The assessment of Title V goals relies on enrollment, retention, and graduation rates which reflect organizational outcomes that policymakers prioritized without deference to student population, institutional mission, and funding levels. Title V policymakers do not currently consider the ways HSIs centralize the racialized experiences of students and institutions do not uniformly collect or report cultural outcome data despite its relevancy to Hispanic student success. The purpose of this study was to draw on criteria identified in the qualitative literature to quantitatively investigate the Typology of HSI Organizational Identities (Garcia, 2017) as a policymaking tool. A TwoStep cluster analysis was used to determine how well the measured variables represent the conceptual typology constructs. A MANOVA determined the degree cultural outcomes further differentiated HSI clusters. To determine the extent to which institutions centralized the experiences of Hispanic students, a website review was used. The results showed three distinct four-year sub-clusters and three distinct two-year sub-clusters with good silhouette measure of cohesion and separation scores. A statistically significant MANOVA in both sets of sub-clusters revealed, to small effect, that 17% of variance iii in cultural outcomes was explained by cluster assignment. Differences between clusters were detected in five of 15 cultural variables. The findings of this study align with the Typology of Hispanic-Serving Institution Organizational Identities (Garcia, 2017); however, alignments could only be made after rubric-informed website reviews. The typology was limited in its practical use because it currently does not accommodate important sector differences. There is overwhelming evidence that two-year and four-year HSIs are significantly different from one another, thus may benefit from separate treatment in Title V. Current federal data prioritization and collection practices are insufficient to affirm an institution’s ability to serve Hispanic students, and opportunities exist for policymakers to remedy the neglect of cultural outcomes. Although interpretation of the findings is constrained by methodological limitations, the results may be used by policymakers, scholars, and HSI practitioners to tailor efforts designed to truly serve Hispanic students

Acute myocardial infarction patient data to assess healthcare utilization and treatments.

The goal of this study is to use a data mining framework to assess the three main treatments for acute myocardial infarction: thrombolytic therapy, percutaneous coronary intervention (percutaneous angioplasty), and coronary artery bypass surgery. The need for a data mining framework in this study arises because of the use of real world data rather than highly clean and homogenous data found in most clinical trials and epidemiological studies. The assessment is based on determining a profile of patients undergoing an episode of acute myocardial infarction, determine resource utilization by treatment, and creating a model that predicts each treatment resource utilization and cost. Text Mining is used to find a subset of input attributes that characterize subjects who undergo the different treatments for acute myocardial infarction as well as distinct resource utilization profiles. Classical statistical methods are used to evaluate the results of text clustering. The features selected by supervised learning are used to build predictive models for resource utilization and are compared with those features selected by traditional statistical methods for a predictive model with the same outcome. Sequence analysis is used to determine the sequence of treatment of acute myocardial infarction. The resulting sequence is used to construct a probability tree that defines the basis for cost effectiveness analysis that compares acute myocardial infarction treatments. To determine effectiveness, survival analysis methodology is implemented to assess the occurrence of death during the hospitalization, the likelihood of a repeated episode of acute myocardial infarction, and the length of time between reoccurrence of an episode of acute myocardial infarction or the occurrence of death. The complexity of this study was mainly based on the data source used: administrative data from insurance claims. Such data source was not originally designed for the study of health outcomes or health resource utilization. However, by transforming record tables from many-to-many relations to one-to-one relations, they became useful in tracking the evolution of disease and disease outcomes. Also, by transforming tables from a wide-format to a long-format, the records became analyzable by many data mining algorithms. Moreover, this study contributed to field of applied mathematics and public health by implementing a sequence analysis on consecutive procedures to determine the sequence of events that describe the evolution of a hospitalization for acute myocardial infarction. This same data transformation and algorithm can be used in the study of rare diseases whose evolution is not well understood

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

A Critical Analysis of the University of Georgia\u27s Response to the United States Supreme Court Decisions in Grutter v. Bollinger and Gratz v. Bollinger

Minority enrollments at selective colleges and universities have historically been low. Affirmative action programs have been a primary driver for increasing enrollments. These programs were called into question in the Grutter and Gratz US Supreme Court cases (2003). The Court’s opinions in these cases provide direction for institutions in setting admissions policy. Using a qualitative methodology, this study examined the University of Georgia’s response to the Grutter and Gratz Supreme Court decisions. The study utilized data from interviews with UGA officials, as well as documentary evidence, to chronologically reconstruct the actions that UGA initiated following the Grutter and Gratz decisions. The study utilized a narrative analytic approach to analyze UGA rationale for its action. It assessed officials’ statements to identify dominant narratives related to the use of race in admissions at UGA. This study positioned the dominant narratives of officials’ relative to competing understandings of admissions, race and the law extracted from the scholarly literature. A metanarrative was developed to highlight commonly held assumptions in the debate around the use of race in higher education admissions. The metanarrative was found to be a useful tool for managing competing perspectives in efforts to develop viable policy approaches for admissions in the future. The study is important in at least two ways: 1) it explains sources of conflict in the affirmative action debate and 2) it suggests the usefulness of narrative policy analysis for policy making related to race, diversity, and admissions in higher education

State political culture and the affordability of higher education : a multivariate analysis of the impact of state higher education boards on the cost of attending college.

The purpose of this study was to understand variance in state system performance of affordability using variables describing the state political environment and the structure of state higher education boards. The researcher utilized the National Center for Public Policy in Higher Education\u27s Measuring Up (2006) grade for the dependent variable. Three control variables were examined: (a) institutional strength of the governor, (b) professionalism of the state legislature, and (c) impact of the special interest groups. The independent variable was state higher education boards. Three levels existed for this variable: (a) consolidated governing board, (b) coordinating board, and (c) planning/service agency. Through examining the independent variable and the control variables that impacted affordability across the 50 states, it was evident that the results did not support research question one. Governance structure was not a significant predictor of affordability. The results of question two showed that professionalism of the state legislature was the most significant predictor of affordability across the three years in question, 2002, 2004, and 2006. Based on the results of the study, the researcher anticipates that policy makers will now spend less time focusing on governance structure and more time shedding light on why professionalism is so important to affordability of higher education across the 50 states

Rehabilitation Outcome Following Acute Stroke: Considering Ideomotor Apraxia

Stroke is a leading cause of death and the leading cause of adult disability in the United States affecting approximately 795,000 people yearly. Stroke sequelae often span multiple domains, including motor, cognitive, and sensory subsystems. Impairments can contribute to difficulty participating in activities of daily living (ADLs) and translate into disability - a concern for patients and occupational therapists alike. The role of ideomotor apraxia (IMA) in stroke rehabilitation is unclear. Thus, the purpose of these two studies is to investigate stroke rehabilitation outcome while considering the presence of ideomotor apraxia. Stroke causes dysfunctional movement patterns arising from an array of potential etiologies. Agreement exists that understanding the patient's functioning serves as the basis for the rehabilitation process and it is insufficient for clinicians simply to determine functional movement problems without knowing how underlying impairments contribute. Stroke-induced paresis is a prevalent impairment and frequent target of traditional rehabilitation. Stroke rehabilitation often addresses paresis narrowly with little consideration for other stroke consequences. Ideomotor apraxia is one such disorder after stroke that could conceivably limit rehabilitation benefit of otherwise efficacious treatment interventions aimed at remediating paresis. This led us to an initial study of a subject who experienced a single left, ischemic stroke with paresis of his right upper extremity and comorbid ideomotor apraxia. The subject participated in combined physical and mental practice for six consecutive weeks to improve use of his right arm. After intervention, the subject demonstrated clinically significant improvements in functional performance of his more-affected right upper extremity and reported greater self-perception of performance. The subject continued to demonstrate improvements after four weeks with no intervention and despite persistent IMA. This single case report highlights the importance of recognizing that ideomotor apraxia does present after stroke, and traditional stroke rehabilitation efforts directed at paresis can be efficacious for subjects with IMA. Traditional beliefs suggested that ideomotor apraxia does not translate to disability in everyday life and that IMA resolves spontaneously. Despite accumulating evidence of the influence of IMA on functional ability, this topic remains relatively neglected. It is unclear how ideomotor apraxia affects the rehabilitation process. The second study reports rehabilitation outcomes of a group of subjects following acute stroke. The Florida Apraxia Battery gesture-to-verbal command test was used to detect IMA in subjects. Level of independence with a set of ADLs and motor impairment of the more-affected upper extremity was documented at admission and discharge. Study subjects participated in standard of care stroke rehabilitation in the inpatient rehabilitation units. A total of fifteen subjects who sustained a left hemisphere stroke participated in this study - ten with IMA and five without IMA. After rehabilitation, subjects with IMA improved ADL independence and displayed decreased motor impairment of their right upper extremity. Subjects with and without IMA exhibited comparable improvements in ADL independence, but subjects with IMA exhibited less ADL independence upon when compared to subjects without IMA. Additional findings suggested that subjects with IMA were not different with respect to motor impairments and length of stay; however, additional studies with larger sample sizes are needed. In summary, these two studies aid to elucidate the implications of ideomotor apraxia on traditional stroke rehabilitation efforts. Study subjects with ideomotor apraxia after acute stroke still derive benefit from traditional rehabilitation. Because traditional rehabilitation interventions narrowly target motor impairment, these findings support the need for considering IMA as a factor in developing interventions tailored to patients with IMA and possibly as a specific focus for interventions. A step toward addressing this need is to assess whether IMA is present after stroke on a regular basis. This work provides a framework for researchers and clinicians to investigate further how ideomotor apraxia translates into disability. These findings are important since consideration of ideomotor apraxia could influence selection and design of rehabilitation interventions to optimize patient daily functioning after stroke