Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Design of an ontology for decision support in VR exposure therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare. Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy. However, adapting the VR environment to the needs of the patient is time-consuming. Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy. In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR. The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology. Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data. The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy

A Colour Design Tool Based on Empirical Studies

A colour design tool targeted at both unskilled consumers and professional designers is currently under development, on the basis of psychophysical studies into semantic associations of colour, the cultural influences and colour harmony. From experimental results for single-colour associations, 3 underlying factors were identified: “warm-cool”, “heavy-light” and “active-passive”, which were found to agree well with those identified by earlier research. For colour-combination associations, an “additive property” of colour association was discovered: the semantic score of a colour combination can be determined by averaging semantic scores of each constituent colour in that combination. According to the experimental results, there were 4 general patterns of colour harmony: similarity in hue and chroma, difference in lightness, high lightness and the hue effects. While the proposed colour design tool is still in its development stage and has a number of shortcomings, the system is believed to provide practical assistance and support not only for unskilled users but also for designers. Keywords: colour design; colour harmony; colour association; cross-cultural study; e-shopping; colour decision-making; design process; psychophysical method</p

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

A semantic web approach for built heritage representation

In a built heritage process, meant as a structured system of activities aimed at the investigation, preservation, and management of architectural heritage, any task accomplished by the several actors involved in it is deeply influenced by the way the knowledge is represented and shared. In the current heritage practice, knowledge representation and management have shown several limitations due to the difficulty of dealing with large amount of extremely heterogeneous data. On this basis, this research aims at extending semantic web approaches and technologies to architectural heritage knowledge management in order to provide an integrated and multidisciplinary representation of the artifact and of the knowledge necessary to support any decision or any intervention and management activity. To this purpose, an ontology-based system, representing the knowledge related to the artifact and its contexts, has been developed through the formalization of domain-specific entities and relationships between them

Semantic-driven knowledge-enabled cognitive decision support system

University of Technology, Sydney. Faculty of Engineering and Information Technology.The importance of knowledge and cognition in business intelligence and decision support systems (DSS) is indisputable. However two major issues, a) biases in cognition, and b) knowledge integration overhead in knowledge warehousing, hinder their optimum utility in such systems. We address the issue of biases by proposing semantic de-biased associations (SDA) model, which is an improvement over the conventional causal map representation of mental models. SDA model incorporates semantics and contextual information to implement automated de-biasing by employing de-biasing techniques and algorithm into the inherent process of mental model elicitation, storage and retrieval. An elicitation process customised for SDA-based representation was also proposed namely SDA articulation and elicitation cycle. SDA model automates the process of mental model validation and integration, so as to prevent any espoused theories to be stored in the system. It also provides faster access to relevant knowledge, while creating a knowledge cycle between user and the system, which provides learning and knowledge growth opportunities to the system users, promoting organizational learning. The issue of knowledge integration overhead is dealt with by proposing a unified, standard storage structure for knowledge warehousing in subject-oriented semantic knowledge warehouse (SSKW). The unified storage structure is achieved through categorising knowledge on syntactic level, and creating universal templates of these categories. In addition, the rules of how they can be connected together are outlined. The categories of knowledge, formalised, are object, process, and event. The connections between them are implemented through semantic relationships. The SSKW provides a domain-independent knowledge warehousing architecture to store knowledge in a subject-oriented, semantic, integrated, systematic and meaningful manner. It incorporates object-oriented, semantic, and human-centric approaches to facilitate an intuitive and efficient communication. It prevents loss of knowledge, improves precision of output, and ensures efficient delivery of knowledge when required. The SDA model and SSKW are integrated together in this research to form a human-centric DSS, semantic-driven knowledge-enabled cognitive decision support system (SCDSS). SCDSS accumulates knowledge of many decision makers over time, thus if a decision maker leaves the organisation, his/her knowledge is retained through this system. Moreover, it automates the dissemination of knowledge across the organisation. Two evaluations were conducted to measure the performance of SCDSS against selected criteria. The results of the evaluations show that SCDSS successfully mitigates availability, framing, contextual and group biases, and generates new knowledge during decision making process. The results also demonstrate the effectiveness of SCDSS in knowledge sharing and enhancement, efficiency in producing output; and the relevance of knowledge in the output. The system can be accessed at http://tasneememon.com/SCDSS/index.php

Gender equity in disaster early warning systems

Capacities of societies, communities and individuals or a social-ecological system to deal with adverse consequences and the impacts of hazard events define the resilience. New and innovative Emergency Communications, Warning Systems (ECWS) technologies and solutions improve resilience of the nations. Research shows that different types of systems (e.g. decision support, resource management, early warning, communications, and inter-agency) are highly valued in emergency and disaster events reducing live losses. As many individuals have online access today and young women have increased their online communication and young men tend to explore technology resources, the potential of using user friendly third revolution digital technology such as semantic features and devices (e.g. SMART phones) have the potential to improve the access to early warning/risk in-formation supporting community decision making saving lives. These personal and social relations that reflect gender dimensions can certainly be examined improving resilience making communities more prepared for disasters with proactive decision making for early warning. Fostering awareness about gender equity which is the recognition of women and men as active participants in development can tailor made within the context of resilience and more specifically within early warning systems saving lives of the people at immediate risk including the dependence of mother’s care (children and older people). In this context, this paper attempts to synthesis literature on the topic of gender equity within disaster early warning systems

Semantic agent architecture: embedding ontology into the agent's reasoning engine

This research presents the development of semantic agent architecture which incorporates semantic technology that allows decision making, reasoning and learning. The agent architecture is the software architecture that is intended to support decision making process for intelligent agent. From the review of existing agent architectures, BDI architecture is chosen to incorporate semantic technology due to the widely adoption of BDI architecture. The BDI architecture is based on the practical reasoning and mentalistic notion. Semantic technology is set of technologies that make data more easily machine-processable. Thus, by incorporating semantic technology into BDI architecture, a semantic agent architecture that allows decision making, reasoning and learning is created. This study illustrates the semantic agent architecture through simple trading system. The trust and reputation are augmented into the agent architecture to allow the agent to evaluate the performance of the other agent

A Mediation Framework for Web Services in a Distributed Healthcare Information System

Conceptualizing distributed Healthcare Information System is an important step toward the enhancement of clinical decision support system. In this paper, we propose a semantic mediation of Web Services interfaces for distributed healthcare system. Our proposal is an approach based on Web Services technology and their mediation in a Peer to Peer environment. This approach constitutes the foundation for the set-up of a mediation framework built around the JXTA P2P architecture applied to cardiology domain in collaboration with the National Institute of Health and Medical Research (INSER ERM 107). To achieve our goal, we used the OWL-S language as a means of describing semantics of Web Services interfaces, and the JXTA distributed architecture. © 2005 IEEE