An Ontology Approach for Knowledge Acquisition and Development of Health Information System (HIS)

This paper emphasizes various knowledge acquisition approaches in terms of tacit and explicit knowledge management that can be helpful to capture, codify and communicate within medical unit. The semantic-based knowledge management system (SKMS) supports knowledge acquisition and incorporates various approaches to provide systematic practical platform to knowledge practitioners and to identify various roles of healthcare professionals, tasks that can be performed according to personnel’s competencies, and activities that are carried out as a part of tasks to achieve defined goals of clinical process. This research outcome gives new vision to IT practitioners to manage the tacit and implicit knowledge in XML format which can be taken as foundation for the development of information systems (IS) so that domain end-users can receive timely healthcare related services according to their demands and needs

Artificial Intelligence Agents and Knowledge Acquisition in Health Information System

This research work highlights the need for AI-powered applications and their usages for theoptimization of information flow processes in the medical sector, from the perspective of howAI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessaryinformation in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times,overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospi-tal, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We dis-cuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and itsretriev-al in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way

Value Added Conversational AI and Digital Health : An Ontology-Driven Approach

AI-based assistants, such as conversational agents (CAs) and social robots, are becoming increasingly important in healthcare organizations. CAs provide a scalable and cost-effective platform for organizations supporting their employees by retrieving, structuring, and analyzing information to assist work processes. This study targets how knowledge graphs as ontological models manage CA that help improve the patient flow processes and reduce patients’ waiting time in the emergency departments (EDs). We tailored the design thinking (DT) method with modelling workshops employing conceptual modelling (CM) techniques to address these issues. We incorporated a hybrid formal approach of Methontology and Tove methodologies to build design artifacts, develop a goal-oriented interactive conversational system between humans and machines, and support information systems (IS). As a result, this ontology-driven approach contribution helps developers build value-added CAs to facilitate healthcare practitioners and patients. It is helpful for quality care delivery experience and improves bottlenecks in information flow within Eds

Value Added Conversational AI and Digital Health : An Ontology-Driven Approach

AI-based assistants, such as conversational agents (CAs) and social robots, are becoming increasingly important in healthcare organizations. CAs provide a scalable and cost-effective platform for organizations supporting their employees by retrieving, structuring, and analyzing information to assist work processes. This study targets how knowledge graphs as ontological models manage CA that help improve the patient flow processes and reduce patients’ waiting time in the emergency departments (EDs). We tailored the design thinking (DT) method with modelling workshops employing conceptual modelling (CM) techniques to address these issues. We incorporated a hybrid formal approach of Methontology and Tove methodologies to build design artifacts, develop a goal-oriented interactive conversational system between humans and machines, and support information systems (IS). As a result, this ontology-driven approach contribution helps developers build value-added CAs to facilitate healthcare practitioners and patients. It is helpful for quality care delivery experience and improves bottlenecks in information flow within Eds

Battalogy: Empowering Battery Data Management through Ontology-driven Knowledge Graph

Developing a battery ontology to represent battery management knowledge is crucial in the new sustainable and green energy era. As battery production revenue is projected to exceed 300 billion US dollars annually by 2030, researchers are exploring new battery materials, models, standards, and manufacturing processes. AI and ML methods are being employed to manage battery manufacturing and enhance performance. Data representation techniques and formats are important for enhancing the expressiveness of battery data and improving battery quality. This paper presents an ontology for creating a battery knowledge graph to address data interoperability challenges and share battery data among different actors. The battery ontology includes various types of knowledge, such as domain knowledge, battery applications, and core battery-specific knowledge. The ontology was evaluated through competency questions and usability tests. It aims to enhance battery production and design by facilitating efficient communication and data exchange between battery management systems and applications. This research has significant societal, economic, and environmental impacts as it contributes to developing more efficient and sustainable batteries