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

US/UK Mental Models of Planning: The Relationship Between Plan Detail and Plan Quality

This paper presents the results of a research study applying a new cultural analysis method to capture commonalities and differences between US and UK mental models of operational planning. The results demonstrate the existence of fundamental differences between the way US and UK planners think about what it means to have a high quality plan. Specifically, the present study captures differences in how US and UK planners conceptualize plan quality. Explicit models of cultural differences in conceptions of plan quality are useful for establishing performance metrics for multinational planning teams. This paper discusses the prospects of enabling automatic evaluation of multinational team performance by combining recent advances in cultural modelling with enhanced ontology languages

What is a Good Plan? Cultural Variations in Expert Planners’ Concepts of Plan Quality

This article presents the results of a field research study examining commonalities and differences between American and British operational planners’ mental models of planning. We conducted Cultural Network Analysis (CNA) interviews with 14 experienced operational planners in the US and UK. Our results demonstrate the existence of fundamental differences between the way American and British expert planners conceive of a high quality plan. Our results revealed that the American planners’ model focused on specification of action to achieve synchronization, providing little autonomy at the level of execution, and included the belief that increasing contingencies reduces risk. The British planners’ model stressed the internal coherence of the plan, to support shared situational awareness and thereby flexibility at the level of execution. The British model also emphasized the belief that reducing the number of assumptions decreases risk. Overall, the American ideal plan serves a controlling function, whereas the British ideal plan supports an enabling function. Interestingly, both the US and UK would view the other’s ideal plan as riskier than their own. The implications of cultural models of plans and planning are described for establishing performance measures and designing systems to support multinational planning teams

Evidence-based careflow management systems: the case of post-stroke rehabilitation

AbstractThe activities of a care providers’ team need to be coordinated within a process properly designed on the basis of available best practice medical knowledge. It requires a rethinking of the management of care processes within health care organizations. The current workflow technology seems to offer the most convenient solution to build such cooperative systems. However, some of its present weaknesses still require an intense research effort to find solutions allowing its exploitation in real medical practice. This paper presents an approach to design and build evidence-based careflow management systems, which can be viewed as components of a knowledge management infrastructure each health care organization should be provided with to increase its performance in delivering high quality care by efficiently exploiting the available knowledge resources. The post-stroke rehabilitation process has been taken as a challenging care problem to assess our methodology for designing and developing careflow management systems. Then a system was co-developed with a team of rehabilitation professionals who will be committed to use it in their daily work. The system’s main goal is to deliver a full array of rehabilitation services provided by an interdisciplinary team. They are related to identify which patients are most likely to benefit from rehabilitation, manage a rehabilitation treatment plan, and monitor progress both during rehabilitation and after return to a community residence. A model of the rehabilitation process was derived from an international guideline and adapted to the local organization of work. It involves different organizational units, such as wards, rehabilitation units, clinical laboratories, and imaging services. Several organizational agents work within them and play one or more roles. Each role is defined by the goals’ set that she/he must fulfill. Special effort has been given to the design and development of a knowledge-based system for managing exceptions, which may occur in daily medical work as any deviation from the normal flow of activities. It allows either avoiding or recovering automatically from expected exceptions. When they are not expected, organizational agents, with enough power to do that, are allowed to modify the scheduled flow of activities for an individual patient under the only constraint of justifying their decision. After an intensive testing in a research laboratory, the system is now in the process of being transferred in a real working setting with the full support of its future users

Panning for gold: designing pedagogically-inspired learning nuggets

Tools to support teachers and learning technologists in the creation of effective learning designs are currently in their infancy. This paper describes a metadata model, devised to assist in the conception and design of new learning activities, that has been developed, used and evaluated over a period of three years. The online tool that embodies this model was not originally intended to produce runtime executable code such as IMS-LD, but rather focussed on assisting teachers in the thought processes involved in selecting appropriate methods, tools, student activities and assessments to suit the required learning objectives. Subsequently, we have modified the RELOAD editor such that the output from our tool can be translated into IMS-LD. The contribution of this paper is the comparison of our data model with that of IMS-LD, and the analysis of how each can inform the other

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

Copyright ©2014 Zhong, Cangelosi and Wermter.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsThe acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.Peer reviewedFinal Published versio

Hybrid Search: Effectively Combining Keywords and Semantic Searches

This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontologybased search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications