Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

The interorganizational implementation of state health policy: A network approach

This is an implementation analysis of three consecutive state health policies whose goal was to improve access to maternal and child health services in Texas from 1983 to 1986. Of particular interest is the choice of the unit of analysis, the policy subsystem, and the network approach to analysis. The network approach analyzes and compares the structure and decision process of six policy subsystems in order to explain program performance. Both changes in state health policy as well as differences in implementation contexts explain evolution of the program administrative and service unit, the policy subsystem. And, in turn, the evolution of the policy subsystem explains changes in program performance. ^

Relating one's social support network to one's mid term grade

Much has been written about the relation of social support to health outcomes. Support networks were found to be predictive of health status. Not so clear was the manner in which social support helped the individual to avoid health complications. Whereas some aspects of the support network were protective, others were burdensome. Duties to one's network could serve as a stressor and duties outside one's network might stress the support system itself. Exposure to one's network was associated with certain health risks while disruption in one's social support network was associated with other health risks.^ Many factors contributed to the impact of a social support network upon the individual member: the characteristics of the individual, the individual's role or position within the network, qualities of the network and duties or indebtedness of the individual to the network. This investigation considered the possibility that performance could serve as a stressor in a fashion similar to an exposure to a health hazard.^ Because the literature includes many examples of studies in which the subjects were college students, academic progress is a performance common to most subjects. A profile of the support networks of successful students was contrasted with those of less successful students in this correlational study.^ What was uncovered in this investigation was a very complex web of interrelated constructs. Most aspects of the social support network did not significantly predict academic performance. Only a limited number of characteristics were associated with academic success: the frequency of support, student age, the existence of a 'mentor' within one' s network, and the extent to which one received a predominant source of support. Other factors had a tendency to be negatively correlated with midterm grade, suggesting those factors may impede academic performance.^ Medical status did not predict grades, but was correlated with many aspects of the network. Disruptions in particular parts of one's network were correlated with particular health categories. In fact, disruption in social support was more predictive of academic outcomes than medical complications. Whereas the individual's values were related to the contributing factors, only the individual's satisfaction with certain aspects of the support network were predictive of higher midterm grades in a psychology class. Dissatisfaction was associated with lower grades, suggesting a disruptive effect within the network. Associations among the features of support networks which predicted academic progress were considered. ^

Assessing the relationship between board composition and nonprofit hospital financial performance from a multi-theoretic perspective

This dissertation assesses the relationship between board composition and financial performance for the top 71 major nonprofit hospitals in the United States during the period 2004-2009. The underlying data were collected from copies of IRS Form 990 available at http://www.guidestar.org . The dissertation investigates five factors: board size, board independence (percentage of outsiders), number of MDs, CEO succession and CEO compensation. And it evaluates the results within a multi-theoretic framework drawing on agency theory, resource dependence theory, institutional theory and social network theory. Corporate governance literature suggests that board composition has an important impact on firm financial performance. This dissertation examines whether the same may be true for nonprofit hospitals. The results should help hospital executives make better governance decisions during trying economic times.^