CASESIAN: A knowledge-based system using statistical and experiential perspectives for improving the knowledge sharing in the medical prescription process

Part 1 Putting knowledge-based systems in context: artificial intelligence - a brief overview knowledge-based systems other fruits of AI research an applications point of view differences between KBS's and conventional computer systems. Part 2 Knowledge-based systems - a solution in search of a problem?: what sort of problems may be addressed using KBS's what factors determine an application's suitability for a KBS approach? operational KBS's the key features of KBS construction. Part 3 KBS/AI concepts and tools: KBS concepts tools used to build KBS's - an overview knowledge acquisition knowledge representation system execution - for the knowledge engineer system execution - for the end-user maintenance. Part 4 Conventional software engineering: why are structured development methods relevant to KBS's? the conventional systems development life-cycle (SDLC) and formal methods extensions to the SDLC prototyping for decision support systems prototyping for non-DSS's contrasting features of DP and KB system development prototyping in KBS development. Part 5 ORSA methodology: why worry about the business objectives? what is operational research? a soft OR methodology soft systems methodology soft methodologies in general. Part 6 Lessons from other KBS work: current KBS development methodology - the textbook view AI methodology compared with DP methodologies a project management perspective. Part 7 The Intellipse experience: the original project specification - the main project phase the key changes to the proposal the concept of Intellipse functional specification the task compraring SSD Intellipse architecture knowledge bases is SSD a suitable domain for KBS's? advisor designer. Part 8 Measuring the success of a knowledge-based system : Why measure the success? what needs to be done? how to measure the success-sources of inspiration how to measure the success of a KBS-our recommendations. Part 9 A methodology for the practical engineering of knowledge-based systems: a POLITE engineering methodology for KBSs' standards for the POLITE methodology-the ITAM investigation POLITE in action.

A coupled knowledge-based expert system for design of liquid-retaining structures

Spirituality is integral to health and wellbeing and a fundamental element of nursing care. Nonetheless, evidence suggests that spirituality is a frequently ignored aspect of nursing education and care. From 2008 to 2010 a Glaserian grounded theory design was used to explore and explain pre-registration nursing students’ personal understanding of their own spirituality and the relationship between experiences in clinical practice and spiritual awareness. While there is evidence that examines relevance of providing spiritual care to service users, at that time, minimal research has been undertaken to examine spiritual needs in pre-registration nursing students. A theory of carrying hope emerged from the findings to explain how pre-registration nursing students resolve clinical experiences with spiritual awareness. The findings identified that pre-registration nursing students’ awareness of spirituality can be explained in three main Basic Social Processes (BSPs): struggling, safeguarding and seeking. This study highlights the extreme personal challenge pre-registration nursing students experience as a result of their experiences in clinical practice and the impact this has upon their spiritual awareness. Recommendations from this study include the implementation of a model of pastoral care for tutors to support spiritual needs of during transition from student to registration.

Decision support systems under multiple objectives (MODSS) has been classified as a specific type of system within the broad family of DSS and has been one of the most active areas of research. However, despite its theoretical development large scale real-world applications of multiobjective decision-making (MODM) methods had been seriously lacking due to the technical knowledge and expertise needed to select and apply the most appropriate method. This paper explores the possibility of embedding intelligent guidance within MODSS, and outlines a specific guidance framework for the design of knowledge-based guidance for the selection of a suitable MODM method. This framework has been implemented as an intelligent and graphical user interface based multiple objective decision support system prototype. The prototype, with its database, methodology base of MODM and knowledge based system, will help any decision-maker in selecting the most appropriate method in solving various MODM problems.

This chapter explains how a mission critical Knowledge-Based System (KBS) has been designed and implemented within a real case study of a governamental organization. Moreover, the KBS has been developed using a novel agile software development methodology. Due to fast changing operational scenarios and volatile requirements, traditional procedural development methodologies perform poorly. Thus, an Agile-like methodology has been exploited, called iAgile. The KBS is based on an ontology used to merge different mental models of users and developers. Moreover, the ontology of the system is useful for interoperability and knowledge representation. Mission critical functionalities have been developed in 5-week cycles, along with the ontology. So, the KBS serves for three main activities: (i) requirement disambiguation, (ii) interoperability with other legacy systems, and (iii) information retrieval and display of different informative sources.

A use of case-based reasoning technique in building expert systems

The development of a knowledge-based software package for the analysis and control of electric distribution networks is discussed. A relational knowledge base, implemented in Prolog, is used to insure fast access to the data and an efficient post-reconfiguration update algorithm. An expert system shell DECIDE, is used to interpret human knowledge coded as knowledge clusters, and to provide a menu-driven user interface. The knowledge-based system described here has the following advantages: its expert system shell provides 'backup' and 'explanation' facilities; it can generate all the possible network reconfigurations and recommend the best choice; and the Prolog implementation of the algorithms is easy to maintain. The knowledge base is easy to expand and can be modified during execution. The prototype system demonstrates that artificial intelligence techniques can be used in power system analysis and reconfiguration, and provides a platform on which a base future research on the utility of expert system technology in distribution automation. >

A prototype of knowledge-based decision support system for construction cost management

This paper presents the coupling of the recent advancements in artificial intelligence (AI) technology with existing numerical models to constitute an integrated knowledge-based system (KBS) on flow and water quality. A hybrid application of the latest AI technologies, namely, KBS, artificial neural network and fuzzy inference system, in this specific problem domain is adopted here. This prototype system, serving as both a design aid as well as a training tool, enables hydraulic engineers and environmental engineers to become acquainted with up-to-date flow and water quality simulation tools, and fill the existing gaps between researchers and practitioners in the application of recent technology in solving real prototype problems in Hong Kong. Moreover, the system can meet the demand for an integrated system that can quickly assist policy makers in reaching decisions and furnish convenient and open information service on water quality for the general public.

Assessing the Client's Risk Management Performance in Construction Procurement and Contracting: Case Studies

One of the most acute problems in the healthcare industry – the problem of the quality of medical services. In this area, there is no established definition of medical services or approaches to quality management. The aim of the article is to analyze the existing definitions of “quality of medical services,” as well as development approach to managing medical organization. At the same time the management of the medical organization should be focused on ensuring the quality as the most important criterion for the organization. Methodology of the study is based on analysis and grouping of existing definitions of medical services, the factorial approach to evaluating the quality and organization of the process approach to management of the medical organization. Noting the versatility and diversity concepts of quality of care the authors suggest grouping of direct and indirect factors affecting the quality of medical services. However, it is important, according to the authors, to move from functional management to management based on the process approach, which provides better control over the processes of customer service. Is given process model of quality management of health services and highlights the main groups of processes in the medical organization.

Knowledge-based information retrieval

The healthcare industry continues to depend extensively on paper-based prescriptions, frequently transmitted through fax systems, to ensure compliance with stringent security and regulatory standards. This reliance introduces significant challenges to manual processing, which is often labor-intensive, error-prone, and inefficient, thereby increasing the risk of medication errors and compromising patient safety. Optical Character Recognition (OCR) technology offers a promising solution by automating the conversion of handwritten and printed prescription images into machine-readable text, thereby enhancing the accuracy and efficiency of healthcare workflows. This paper presents a detailed review of OCR methodologies, focusing on feature extraction, classification, and segmentation techniques that are critical for achieving high recognition accuracy in complex handwritten texts, such as medical prescriptions. By leveraging advanced pattern recognition approaches, including neural networks and hybrid models, OCR systems can significantly reduce manual effort and errors in prescription processing. This study emphasizes the importance of selecting relevant feature extraction methods and robust classification algorithms to handle the variability and noise inherent in re-faxed prescriptions, thereby improving the reliability of automated prescription recognition systems. This study aims to guide and update researchers and practitioners in developing efficient OCR solutions tailored to the healthcare domain, ultimately contributing to safer and more efficient medication administration processes.

A Computational Knowledge Elicitation and Sharing System for mental health case management of the social service industry

The quality of model-based definition (MBD) models is one of the significant challenges to achieving model-based enterprise (MBE). Different stakeholders in MBE put forward different quality requirements for MBD models, while any quality defect may hinder the production. Therefore, it is essential to analyze the quality of MBD models adequately before the usage. However, existing quality tools are difficult to apply in MBE. A knowledge-based quality analysis system for MBD part models is presented in this paper. The system utilizes model quality knowledge to analyze model quality from the perspective of various stakeholders in MBE. And it can be extended by adding new MBD part model quality knowledge. In order to judge model quality, a concept of the model definition unit is introduced for converting model information into the description towards model semantics quality, and a quality analysis method based on this description is proposed. For the ease of model quality knowledge representation and maintenance, a knowledge representation framework based on the object chain and parameter table is developed. Finally, a prototype system is implemented to verify the effectiveness and practicality. With 46 knowledge items derived from eight model-used stages, in total 24 potential quality defects are found in two case models. And additional two defects are discovered by extending the system with new model quality knowledge. In this way, the model quality is improved from the perspective of corresponding users.