The intention of this master´s thesis is to analyze order processing in chosen company with focusing on sales activities. Subjects of our investigation are sales department processes. Object of the research is company AHP HYDRAULIKA, a. s. and it´s sales department. In the thesis are identified failure processes and contributions of this diploma´s thesis are specific proposals how to improve activities and processes at the sales department and elimination of insufficient and failure processes.

Learning representations of medical concepts from the Electronic Health Records (EHRs) has been shown effective for predictive analytics in healthcare. The learned representations are expected to preserve the semantic meanings of different medical concepts, which can be treated as features and thus benefit a variety of applications. Medical ontologies have also been explored to be integrated with the EHR data to further enhance the accuracy of various prediction tasks in healthcare. Most of the existing works assume that medical concepts under the same ontological category should share similar representations, which however does not always hold. In particular, the categorizations in the categorical medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns in the EHR data, leading to contradicting objectives for the representation learning. In addition, these existing works merely utilize the categorical ontologies. Actually, it has been noticed that ontologies containing multiple types of relations are also available. However, studies rarely make use of the diverse types of medical ontologies. In this thesis research, we propose three novel representation learning models for integrating the EHR data and medical ontologies for predictive analytics. To improve the interpretability and alleviate the conflicting objective issue between the EHR data and medical ontologies, we propose techniques to learn medical concepts embeddings with multiple ontological representations. To reduce the reliance on labeled data, we treat the co-occurrence statistics of clinical events as additional training signals, which help us learn good representations even with few labeled data. To leverage the various domain knowledge, we also consider multiple medical ontologies (CCS, ATC and SNOMED-CT) and propose corresponding attention mechanisms so as to take the best advantage of the medical ontologies with better interpretability. Our proposed models can achieve the final medical concept representations which align better with the EHR data. We conduct extensive experiments, and our empirical results prove the effectiveness of the proposed methods. Keywords: Bio/Medicine, Healthcare-AI, Electronic Health Record, Representation Learning, Machine Learning Applications

No description available.

Thesis (Ph. D.)--University of Southern California.

Health numeracy is a necessary skill for accessing health services. Immigrants have lower levels of health numeracy compared to host populations which constrains their access to health information necessary to make quality health decisions. Factors contributing to immigrants??? low health numeracy skills include language and mathematics self-efficacy. Language is associated with how people acquire and process numeric information. Some languages have more numeric concepts than others. Speakers of languages that lack one or more numeric concepts may be constrained in the comprehension of health information that contains such concepts. Moreover, they may lack the self-efficacy to engage in numeric tasks containing such concepts. Therefore, the overall objectives of this study were: 1) to investigate the effect of primary language and 2) mathematics self-efficacy on its speakers??? comprehension of numeric health information presented in a different language; and also 3) to investigate how speakers of low and high numeric concept languages process numeric health information when the information is presented in a language which is not their primary or first language. The study involved sixty Kikuyu (a low numeric concept language) and sixty Mandarin (a high numeric concept language) speaking immigrants to Canada. Demographic data was collected from the 120 participants using a general information questionnaire. Numeracy was assessed using a context-free numeracy tool (French Kit). Short test of functional literacy in adults (S-TOFHLA) and the newest vital signs (NVS) were used to assess health numeracy and literacy, and self-efficacy was measured with the Mathematics Self-Efficacy Scale (MSES) and the Subjective Numeracy Scale (SNS). Processing of numeric health information was assessed qualitatively using the think-aloud method. Descriptive statistics were generated for performance in numeracy, health numeracy and literacy, and in mathematics self-efficacy. Multiple regression analysis was conducted to determine the predictors of numeracy and health numeracy. Protocol analysis was conducted for the verbal information obtained from the think-aloud process. Results, interpretations and implications for public health practice and research are discussed.

Thesis (M.S.)--Rochester Institute of Technology, 1987. / Typescript. Includes bibliographical references (leaves 37-39).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 69-76). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 43-50). Also available on the Internet.

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed September 20, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 238-244).

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 88 pages. Includes vita. Includes bibliographical references.