Conceptual knowledge acquisition in biomedicine: A methodological review

AbstractThe use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain

Improving the Knowledge-Based Expert System Lifecycle

Knowledge-based expert systems are used to enhance and automate manual processes through the use of a knowledge base and modern computing power. The traditional methodology for creating knowledge-based expert systems has many commonly encountered issues that can prevent successful implementations. Complications during the knowledge acquisition phase can prevent a knowledge-based expert system from functioning properly. Furthermore, the time and resources required to maintain a knowledge-based expert system once implemented can become problematic. There are several concepts that can be integrated into a proposed methodology to improve the knowledge-based expert system lifecycle to create a more efficient process. These methods are commonly used in other disciplines but have not traditionally been incorporated into the knowledge-based expert system lifecycle. A container-loading knowledge-based expert system was created to test the concepts in the proposed methodology. The results from the container-loading knowledge-based expert system test were compared against the historical records of thirteen container ships loaded between 2008 and 2011

COMMUNICATION OF MIS RESEARCH: AN ANALYSIS OF JOURNAL STRATIFICATION

The stratification among journals constituting the formal communication system for MIS research is described and analyzed on the basis of MIS experts\u27 opinions, published MIS articles, and citation frequency. Implications of the research results are discussed for authors seeking suitable publication outlets, for academic administrators making promotion decisions, for editors wishing to establish coverage policy, and for librarians making journal acquisition decisions

Operations research and computers

operational research

EVALUACIÓN COMPUTARIZADA DE PRUEBAS PSICOLÓGICAS MEDIANTE EL PROCESAMIENTO DIGITAL DE IMÁGENES

Este trabajo presenta un algoritmo desarrollado en MATLAB® para la evaluación computarizadade pruebas psicológicas realizadas con la técnica de lápiz-papel de opción múltiple. Con la utilización adecuada de las herramientas actuales para el procesamiento digital de imágenes y reconocimiento de patrones, es posible la evaluación automática de cualquier prueba psicológica realizada con la técnica de lápiz-papel utilizando una computadora personal convencional. En este caso, para demostrar el potencial del algoritmo, fue adaptado para la escala básica del Inventario Multifásico de la Personalidad de Minnesota para individuos masculinos. Los resultados obtenidos demuestran 99.5% de exactitud y la obtención de la gráfica del perfil en 40 segundos. Con el formato digitalizado de la hoja de respuestas, el algoritmo realiza el acondicionamiento de la imagen, la detección y clasificación de los ítems y la contabilidad de las respuestas; finalmente, brinda la gráfica del perfil del individuo

The Role of ICT in Sustainable Development: The Ugandan Narrative

This article investigates the effectiveness of information and communications technologies in sustainable development among nonOECD economies that encapsulate its underlying beliefs and values. Specifically, we have developed a data-driven narrative by using Uganda as the subject of case analysis. We performed statistical analysis to study the significance of ICT as an enabler to the overall socio-economic development. There is a missing component comprising digital literacy and participation that have calls into question the mediating role of inclusive growth. Both our method and findings adopt generally accepted protocols for case studies. The contribution of this research is interpretivist in nature, and we recommend that similar studies need to be replicated by researchers across geographies and domains

Towards Knowledge Driven Decision Support for Personalized Home-based Self-management of Chronic Diseases

The use of ICT technologies to facilitate self-management for patients with chronic diseases attracts increasing attention in smart healthcare. Existing research has mainly focused on sensing and data processing technologies with little work on decision support mechanisms and systems. In this paper, we propose a home-based decision support system based on a wide range of assessment metrics from medical assessment, social and psychological evaluation to behaviour analysis to help self-manage rehabilitation and wellbeing in a personalized manner for different patients. This paper develops semantic models for describing patients, their conditions, medical and behavioural assessments and inference mechanisms for decision recommendations. The research is undertaken in the context of mobile user self-management for Spondyloarthritis (SpA) patients. A case scenario is used to demonstrate the application of the proposed approach, technologies and principles

The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database

Missing data creates various problems in analysing and processing data in databases. In this paper we introduce a new method aimed at approximating missing data in a database using a combination of genetic algorithms and neural networks. The proposed method uses genetic algorithm to minimise an error function derived from an auto-associative neural network. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are employed to train the neural networks. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior toMLP