Background: Preventable medical error accounts for approximately 98,000 deaths in the hospital setting each year. A proposed solution to decreasing medical error encompasses the development of a culture of safety. Safety culture has been defined as a common set of values and beliefs that are shared by individuals within an organization that influence their actions and behaviors. In 2015, the safety culture of Registered Nurses (RN) and Patient Care Technicians (PCT) who regularly worked in the Intensive Care Unit (ICU) and Cardiovascular Intensive Care Unit (CVICU) at Flagstaff Medical Center (FMC) was assessed using the Hospital Survey on Patient Safety Culture. This survey functioned as a needs assessment and demonstrated that ICU/CVICU staff had negative reactions to safety culture and error reporting on eight of twelve composites tested. Based off these results, the Incident Decision Tree (IDT) was selected as an intervention to help improve the areas identified in the needs assessment. Purpose: The aims of this quality improvement project included: 1) Development of a protocol for IDT use by ICU/CVICU managers; 2) Implementing the IDT; and 3) Administering a post IDT implementation survey. Methods: The IDT was implemented during a 4-week period in the ICU/CVICU at FMC. During this time, managers used the IDT when processing reported error. Post implementation, an online survey was administered over the course of two weeks to ICU/CVICU managers and unit based RNs and PCTs to reassess their perceptions on the IDT, error reporting, and safety culture. Results: During the implementation period, 23 errors were reported in the ICU/CVICU at FMC with management utilizing the IDT a total of 12 times. Analysis of the reportable data demonstrated that of the 12 incidents, seven were attributed to system failures. The remaining five incidents were processed using the “foresight test.” Conclusions: Results from the post implementation survey demonstrated that ICU/CVICU staff felt the IDT contributed to a non-punitive environment. Staff also reported the IDT helped to increase communication after an error occurred. Lastly, the majority of staff felt the IDT increased transparency in the error reporting process.

Using ontologies is a key technology in the semantic web. The semantic web helps people to store their data on the web, build vocabularies, and has written rules for handling these data and also helps the search engines to distinguish between the information they want to access in web easier. In order to use multiple ontologies created by different experts we need matchers to find the similar concepts in them to use it to merge these ontologies. Text based searches use the string similarity functions to find the equivalent concepts inside ontologies using their names.This is the method that is used in lexical matchers. But a global standard for naming the concepts in different research area does not exist or has not been used. The same name may refer to different concepts while different names may describe the same concept. To solve this problem we can use another approach for calculating the similarity value between concepts which is used in structural and constraint-based matchers. It uses relations between concepts, synonyms and other information that are stored in the ontologies. Another category for matchers is instance-based that uses additional information like documents related to the concepts of ontologies, the corpus, to calculate the similarity value for the concepts. Decision trees in the area of data mining are used for different kind of classification for different purposes. Using decision trees in an instance-based matcher is the main concept of this thesis. The results of this implemented matcher using the C4.5 algorithm are discussed. The matcher is also compared to other matchers. It also is used for combination with other matchers to get a better result.

In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing the interaction among variables. The results show that the proposed methods are efficient and easy to use in comparison to the current machine learning methods.

[pt] O problema de minimizar o custo de avaliar uma função discreta lendo sequencialmente as suas variáveis é um problema que surge em diversas aplicações, entre elas sistemas de diagnóstico automático e aprendizado ativo. Neste problema, cada variável da função está associada a um custo, que se deve pagar para checar o seu valor. Além disso, pode existir uma distribuição de probabilidades associadas aos pontos onde a função está definida. A maioria dos trabalhos nesta área se concentra ou na minimização do custo máximo ou na minimização do custo esperado gasto para avaliar a função. Nesta dissertação, mostramos como obter uma Ômicron logaritmo de N aproximação em relação à minimização do pior custo (a melhor aproximação possível assumindo que P é diferente de NP). Nós também mostramos um procedimento polinomial para avaliar uma função otimizando simultaneamente o pior custo e o custo esperado. / [en] The problem of minimizing the cost of evaluating a discrete function by sequentially reading its variables is a problem that arises in several applications, among them automatic diagnosis design and active learning. In this problem, each variable of the function is associated with a cost, that we have to pay in order to check its value. In addition, there may exist a probability distribution associated with the points where the function is defined. Most of the work in the area has focussed either on the minimization of the maximum cost or on the minimization of the expected cost spent to evaluate the function. In this dissertation, we show how to obtain an Ômicron logarithm of N approximation with respect to the worst case minimization (the best possible approximation under the assumption that P is different from NP). We also show a polynomial time procedure for evaluate a function that simultaneously optimizes both the worst and the expected costs.

This study investigated the influences of personal value systems and risk propensities on managerial decision-making quality during value clashes. The post-globalisation business landscape is impacted by role players of vastly differing personal attributes, hypothesised to have varying influences on decision-making behaviour. A deeper understanding of how these attributes impact decision-making quality will therefore enrich the literature and arm practitioners with improved decision-making skills. A review of behavioural decision-making literature revealed three core approaches: the normative (prescriptive) perspective, focussed on decision analysis, the cognitive limitations perspective highlighting the boundaries of human cognition and the psychological (values/emotions/motivations) perspective allowing for ethical- or value-boundedness. The extant literature contributes little on the quality of decision-making exhibited by managers, or how to improve it. It also doesn’t consider variance in decision-making between groups defined by personal value and risk traits. This study therefore aimed to establish whether decision-making quality varied with variances in personal attributes, and whether an intervention would improve decision-making behaviour. The research, conducted on a sample of 460 South African managers, established the demographics and value- and risk orientations of the participating group. Three value clashing scenarios, incorporating social-relational framing interventions, where introduced to gauge the decision-making behaviour of the test subjects. Decision-making quality was assessed through the integrative complexity measure and qualitative assessments were conducted on the decision motivation texts. Decision-tree analyses, multiple regression analyses as well as T-tests comparing the decision-quality produced by individuals of opposing orientations, revealed a clear relationship between the value segments of self-enhancement and openness to change and higher quality decision-making. Social risk-taking was related to better decision-making and reframing the scenarios produced better decision-making quality responses, if the reframing was done harshly enough. The qualitative analysis supported these findings, but hinted at additional, context specific decision motivators. This study contributed an integrated view of decision-making literature, tested the application of integrative complexity as a measure of decision quality and introduced new perspectives on how value orientations, risk proclivities and scenario framing relate to decision-making quality. Practitioners can apply this to assess individuals in terms of their decision-making abilities, and can improve decision-making quality in managers through scenario re-framing. / Thesis (PhD)--University of Pretoria, 2017. / Gordon Institute of Business Science (GIBS) / PhD / Unrestricted

This thesis deals with category management of baby food on the Czech market. The goal of the thesis is to determine the theoretical basis of category management and define the category management process. Subsequently, based on the analysis of primary and secondary data, will be characterized the change in current shopping behaviour of mothers. Next goal is to design a general model structure of baby food exposure and through this model to determine whether the current situation on the baby food market, in the field of presentation of products, meets the needs and desires of customers. Primary data will be collected through the quantitative on-line questionnaire survey. The source for the secondary analysis will be the data from the project Market&Media&Lifestyle, market data and shopper behaviour research 2013.

The main goal of this paper is the identification of comments on internet websites. The theoretical part is focused on artificial intelligence, mainly classifiers are described there. The practical part deals with creation of training database, which is formed by using generators of features. A generated feature might be for example a title of the HTML element where the comment is. The training database is created by input of classifiers. The result of this paper is testing classifiers in the RapidMiner program.

This master's thesis concerns the use of the methodology of the decision tree in the investment decision making. The proposed decision model provides the best solution from the available options for the EXPECT-IT ltd. company. This thesis contains also a range of own proposals followed by their evaluation.

The objectives of this research were to 1) investigate bridge deck condition assessment methods used in the field and laboratory, methods of managing bridge decks, and methods for estimating remaining bridge deck service life using computer models through a comprehensive literature review on these subjects; 2) collect and analyze field data from representative concrete bridge decks in Utah; and 3) develop a decision tree for concrete bridge deck management in Utah. As a result of the literature review performed for objective 1, a synthesis of existing information about condition assessment, bridge deck preservation and rehabilitation, bridge deck reconstruction, and estimating remaining service life using computer models was compiled. For objective 2, 15 bridge decks were strategically selected for testing in this research. Five bridge decks had bare concrete surfaces, five bridge decks had asphalt overlays, and five bridge decks had polymer overlays. Bridge deck testing included site layout, cover depth measurement, chloride concentration testing, chain dragging, half-cell potential testing, Schmidt rebound hammer testing, impact-echo testing, and vertical electrical impedance testing. Two-sample t-tests were performed to investigate the effects of selected bridge deck features, including polymer overlay application, deck age at polymer overlay application, overlay age, asphalt overlay application with and without a membrane, stay-in-place metal forms (SIPMFs), SIPMF removal, internally cured concrete, and use of an automatic deck deicing system. For objective 3, condition assessment methods were described in terms of test type, factors evaluated, equipment cost, data collection speed, required expertise, and traffic control for each method. Unit costs, expected treatment service life estimates, and factors addressed for the preservation, rehabilitation, and reconstruction methods most commonly used by the Utah Department of Transportation (UDOT) were also summarized. Bridge deck testing results were supplemented with information about current bridge deck management practices and treatment costs obtained from UDOT, as well as information about condition assessment and expected treatment service life, to develop a decision tree for concrete bridge deck management. Based on the results of field work and statistical analyses, placing an overlay within a year after construction is recommended. Removing SIPMFs after a deck age greater than 18 years is not likely to be effective at reversing the adverse effects of the SIPMFs on bridge deck condition and is not recommended. Bridge deck construction using internally cured concrete is not recommended for protecting against rebar corrosion. To the extent that excluding an automatic deck deicing system does not compromise public safety, automatic deck deicing systems are not recommended. To supplement the typical corrosion initiation threshold of 2.0 lb Cl-/yd3 of concrete for black bar, a corrosion initiation threshold of 8.0 lb Cl-/yd3 of concrete is recommended in this research for bridge decks with intact epoxy-coated rebar. For chloride concentrations less than 20 lb Cl-/yd3 of concrete as measured between reinforcing bars, an increase of up to 70 percent should be applied to estimate the corresponding chloride concentration of the concrete in direct contact with the rebar. The decision tree developed in this research includes 10 junctions and seven recommended treatments. The junctions require the user to address questions about surface type, degree of protection against water and chloride ion ingress, degree of deterioration, and years of additional service life needed; the answers lead to selection of treatment options ranging from repairing an overlay to full-depth bridge deck reconstruction. Revisions to the decision tree should be incorporated as additional methods, data, treatments, or other relevant information become available.

Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.