The current generation of Palm PDA devices is designed to share information records primarily with a base desktop system, or a server. Therefore, their built- in features for sharing data during ad-hoc collaboration among groups of mobile users are inadequate. In this thesis, we describe a new framework that addresses this problem by allowing users to transparently share the record databases of common applications during spontaneous collaborative sessions. The framework also allows users to define custom sharing policies for each application/user pair. These policies determine the manner in which records are exchanged and update, thereby automating the process of handling conflicts and preserving user privacy preferences. We also present implementation results, in which we have used the framework to create shared versions of common applications, such as Calendar and Memo. Our experimental results show that the programming effort involved is minimal and the user interaction with the application is, essentially, the same as in the original application.

Thesis (Ed.D.)--Boston University.

To investigate the subjective experience of children diagnosed with anxiety disorders and the presenting complaints that children with anxiety disorders are referred with to a child and adolescent psychiatry unit.

Government initiatives called for electronic health records for each individual healthcare consumer by 2014. the purpose of the initiatives is to provide for the common exchange of clinical information between healthcare consumers, healthcare providers, third-party payers and public healthcare officials.This exchange of healthcare information will impact the healthcare industry and enable more effective and efficient application of healthcare so that there may be a decrease in medical errors, increase in access to quality of care tools, and enhancement of decision making abilities by healthcare consumers, healthcare providers and government health agencies. an electronic personal health record (ePHR) created, managed and accessed by healthcare consumers may be the answer to fulfilling the national initiative. However, since healthcare consumers potentially are in control of their own ePHR, the healthcare consumer’s concern for privacy may be a barrier for the effective implementation of a nationwide network of ePHR. a technology acceptance model, an information boundary theory model and a trust model were integrated to analyze usage intentions of healthcare consumers of ePHR. Results indicate that healthcare consumers feel there is a perceived usefulness of ePHR; however they may not see ePHR as easy to use. Results also indicate that the perceived usefulness of utilizing ePHR does not overcome the low perceived ease of use to the extent that healthcare consumers intend to utilize ePHR. in addition, healthcare consumers may not understand the different components of usage: access, management, sharing and facilitating third-party ePHR. Also, demographics, computer self-efficacy, personal innovativeness, healthcare need and healthcare literacy impact a healthcare consumer’s privacy concerns and trusting intentions in the context of ePHR and intent to utilize ePHR. Finally, this research indicates that healthcare consumers may need a better understanding of the Health Insurance and Portability and Accountability Act of 1996 (HIPAA) regulations of ePHR as well as a better understanding of the impact HIPAA has on websites that may facilitate ePHR.

The first chapter specifies accounting at the very outset, principal terms and factors relating to book-keeping methods, basics requirements for individual accounting forms, data creation, their development, techniques of accountings recordings in all their forms - without using writing, with using writing up to letterpress. Chapter two describes beginning of ancients nations, accounting development and expansion (Babylonit, Egypt, Crete, Izrael, Arabic nations, Phoenician nation, India. Characterization of development in old Greece and Rome includes separate chapters. The third chapter analyzes double-entry accounting development in Europe. Italian book-keeping and books of accounts have here a pivotal part. Particular subchapters are focused on advanced italian book-keepings forms in Germany, England and France. Chapter four solves computers issue in general and identifies instruments envelope from abakus till mechanical machines. Subsequent subchapter describes particular sorts of data carriers. Chapter five describes computers in individual generations -- their technical and technological parameters. Chapter six analyzes PC utilization for, impacts of using information and communication technologies in accounting. Is divided into two parts -- mechanization and automatization.. In the first part "Mechanization" are described individual machines, which were used. In the second part "Automatization" are described two basic data processing acesses. The last chapter describes demanding procees of selection of the right accounting software and determines individual selection criterions.

Alzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of machine learning techniques to augment diagnostic procedures has been previously examined in limited capacity but to date no research examines real-world medical applications of predictive analytics for health records and cognitive exam scores. This dissertation seeks to examine the efficacy of detecting cognitive impairment due to Alzheimer’s disease using machine learning, including multi-modal neural network architectures, with a real-world clinical dataset used to determine the accuracy and applicability of the generated models. An in-depth analysis of each type of data (e.g. cognitive exams, questionnaires, demographics) as well as the cognitive domains examined (e.g. memory, attention, language) is performed to identify the most useful targets, with cognitive exams and questionnaires being found to be the most useful features and short-term memory, attention, and language found to be the most important cognitive domains. In an effort to reduce medical costs and streamline procedures, optimally predictive and efficient groups of features were identified and selected, with the best performing and economical group containing only three questions and one cognitive exam component, producing an accuracy of 85%. The most effective diagnostic scoring procedure was examined, with simple threshold counting based on medical documentation being identified as the most useful. Overall predictive analysis found that Alzheimer’s disease can be detected most accurately using a bimodal multi-input neural network model using separated cognitive domains and questionnaires, with a detection accuracy of 88% using the real-world testing set, and that the technique of analyzing domains separately serves to significantly improve model efficacy compared to models that combine them. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

The purpose of this study was to determine if data routinely collected during the kindergarten year and at entry into first grade could be used to predict whether a child would be perceived as successful or not successful by the end of first grade. The need for immediate continued research on this topic was established through the review of literature, which highlighted the extent of the at-risk problem both locally and nationally. The growing number of at-risk students combined with the minimal impact of the educational programs mandates the need to identify these students in time to prevent school failure. However, clear identification procedures are not currently available and previous studies have raised substantial questions regarding the accuracy of early identification procedures. The presenting problem of this study was to determine the sensitivity and specificity of a set of predictor variables, and then to analyze these findings as to whether or not they were accurate enough for use as an initial identification process for subsequent classes. The primary research approach of this study was a longitudinal data collection and correlational analysis, with discriminant analysis techniques used to determine predictive accuracy. The study was limited to data on the class of 2001 from two elementary schools within the Washougal School District. The data collected and the subsequent analysis were used to answer six exploratory research questions. No hypothesis was proposed. This study used ratings and scores obtained from the administration of the Preschool Screening system, kindergarten teacher ratings, the School Success Rating Scale, and the Gates-MacGinitie Reading Readiness Tests as predictor variables. Criterion measures of school success/failure were: placement into special programs or grade retention, and end-of-first-grade evaluations of individual student success (report cards, teacher ratings, Gates-MacGinitie Reading Achievement, and the School Success Ratings Scale). The demographic variables of gender, age, parent marital status, and eligibility for free or reduced lunch were analyzed for their potential to exceed or enhance the accuracy of the predictor variables. Three types of measurement were defined and required in order for a predictor or predictor combination to be considered adequate for use in an identification process. These were overall accuracy, criterion sensitivity and specificity accuracy, and prediction sensitivity and specificity accuracy. An 80 percent accuracy level was desired on all three types of measurement. Findings of this study indicated that no single or combination of predictor, and/or demographic variables produced all three desired levels of accuracy. Various combinations of the predictor and demographic variables produced overall accuracy rates exceeding 80 percent for each of the criterion variables. Criterion measured sensitivity and specificity were found to be adequate for use in the prediction of at-risk students. Prediction measured specificity was also found to be highly accurate. Prediction sensitivity, however, was below the desired 80 percent level, indicating that the predictor variables over identify at-risk students. It was concluded that the predictor variables could be used in an identification process if mild over-identification of at-risk students was acceptable to the district. Any use of these identification procedures is assumed to be in connection with ethical intervention practices. Recommendations from this study included cross validation of the results and continuation of the study regarding the predictive accuracy of the identified variables as the students move through higher grade levels. The study also encouraged the Washougal School District to develop a formal collection and processing procedure for their routinely collected data.

This problem is to construct a set of cumulative record cards for use in the elementary and secondary schools of Utah. In order to realize the above purpose it will be necessary to (1) make forms for the systematic recording of the data and (2) provide for standardizing methods of collecting these data.

Thesis (Ph. D.)--Virginia Commonwealth University, 2006. / Prepared for: Dept. of Health Administration. Bibliography: leaves 244-253. Also available online.

The problem of records retention is often viewed as simply deleting records when they have outlived their purpose. However, in the world of relational databases there is no standardized notion of a business record and its retention obligations. Unlike physical documents such as forms and reports, information in databases is organized such that one item of data may be part of various legal records and consequently subject to several (and possibly conflicting) retention policies. This thesis proposes a framework for records retention in relational database systems. It presents a mechanism through which users can specify a broad range of protective and destructive data retention policies for relational records. Compared to naïve solutions for enforcing records management policies, our framework is not only significantly more efficient but it also addresses several unanswered questions about how policies can be mapped from given legal requirements to actions on relational data. The novelty in our approach is that we defined a record in a relational database as an arbitrary logical view, effectively allowing us to reduce several challenges in enforcing data retention policies to well-studied problems in database theory. We argue that our expression based approach of tracking records management obligations is not only easier for records managers to use but also far more space/time efficient compared to traditional metadata approaches discussed in the literature. The thesis concludes with a thorough examination of the limitations of the proposed framework and suggestion for future research in the area of records management for relational database management systems.