Clinical Guideline Audit and Knowledge Elicitation Using the MDS Tool and Techniques

This paper outlines a study, utilising the MDS methodology and tool to create a knowledge model based on clinical experts’ interpreted knowledge of clinical guidelines. The study demonstrated the elicitation of tacit expert knowledge when the formalised processes of the MDS were applied to model a clinical expert’s interpretation of the knowledge content of a clinical guideline onto the specialised MDS architecture

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks

Factors driving enterprise adoption of blockchain technology

Amidst the rapidly evolving advancement of blockchain technology (BT), enterprises face notable challenges in leveraging its transformative potential, starting with a need to understand the technology and how it can be used for particular applications. Two challenges are that many BT trials have not been successful and large-scale implementations that have led to continued use are scarce. This research provides a comprehensive examination of factors that drive the successful adoption of BT for enterprise use cases. A dual-phased approach was employed. First, I introduce a taxonomy matrix correlating BT design characteristics with use case characteristics, offering a framework for BT design and benefits across different enterprise contexts. Second, I conducted case studies of five successful BT cases in large enterprises that led to the adoption in terms of continued use and contrasted them with one failure case. The data collection and analysis of the case studies encompassed technological, organizational, environmental, and inter-organizational variables that led to BT\u27s continued use. The cross-case analysis revealed that compatibility, relative advantage, and observability are primary technological factors contributing to continued use. Within the organizational dimension, organizational knowledge and internal characteristics emerged as crucial elements, while regulatory compliance came out to be a significant factor. Based on the cross-case analysis, I develop theoretical propositions about the factors that lead to the continued use of BT, which can be further validated and tested in future research

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic