Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI

International audienceMedical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators and models is described and exemplified on simulated cardiac MRIs and ultrasonic images

Performance Analysis and Assessment of a TF-IDF Based Archetype-SNOMED-CT Binding Algorithm

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors\u27 automatic search and used for assessing the algorithm\u27s performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

Social Media for the Promotion of Holistic Self-Participatory Care: An Evidence Based Approach

Objectives: As health information is becoming increasingly accessible, social media offers ample opportunities to track, be informed, share and promote health. These authors explore how social media and holistic care may work together; more specifically however, our objective is to document, from different perspectives, how social networks have impacted, supported and helped sustain holistic self-participatory care. Methods: A literature review was performed to investigate the use of social media for promoting health in general and complementary alternative care in particular. We also explore a case study of an intervention for improving the health of Greek senior citizens through digital and other means. Results: The Health Belief Model provides a framework for assessing the benefits of social media interventions in promoting comprehensive participatory self-care. Some interventions are particularly effective when integrating social media with real-world encounters. Yet not all social media tools are evidence-based and efficacious. Interestingly, social media is also used to elicit patient ratings of treatments (e.g., for depression), often demonstrating the effectiveness of complementary treatments, such as yoga and mindfulness meditation. Conclusions: To facilitate the use of social media for the promotion of complementary alternative medicine through self-quantification, social connectedness and sharing of experiences, exploration of concrete and abstract ideas are presented herewithin. The main mechanisms by which social support may help improve health - emotional support, an ability to share experiences, and non-hierarchal roles, emphasizing reciprocity in giving and receiving support – are integral to social media and provide great hope for its effective us

A Survey of Access Control Models in Wireless Sensor Networks

Copyright 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/)Wireless sensor networks (WSNs) have attracted considerable interest in the research community, because of their wide range of applications. However, due to the distributed nature of WSNs and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. Resource constraints in sensor nodes mean that security mechanisms with a large overhead of computation and communication are impractical to use in WSNs; security in sensor networks is, therefore, a challenge. Access control is a critical security service that offers the appropriate access privileges to legitimate users and prevents illegitimate users from unauthorized access. However, access control has not received much attention in the context of WSNs. This paper provides an overview of security threats and attacks, outlines the security requirements and presents a state-of-the-art survey on access control models, including a comparison and evaluation based on their characteristics in WSNs. Potential challenging issues for access control schemes in WSNs are also discussed.Peer reviewe

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

Physical human-robot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented

Modelling the impact of climate change on health

The main objective of this thesis is to develop a robust statistical model by accounting the non-linear relationships between hospital admissions due to lower respiratory (LR) disease and factors of climate and pollution, and their delayed effects on hospital admissions. This study also evaluates whether the model fits can be improved by considering the non-linearity of the data, delayed effect of the significant factors, and thus calculate threshold levels of the significant climate and pollution factors for emergency LR hospital admissions. For the first time three unique administrative datasets were merged: Hospital Episode Statistics, Met office observational data for climate factors, and data from London Air Quality Network. The results of the final GLM, showed that daily temperature, rain, wind speed, sun hours, relative humidity, and PM10 significantly affected the LR emergency hospital admissions. Then, we developed a Distributed lag non-linear model (DLNM) model considering the significant climate and pollution factors. Time and ‘day of the week’ was incorporated as linear terms in the final model. Higher temperatures around ≥270C a quicker effect of 0-2 days lag but lower temperatures (≤00C) had delayed effects of 5-25 days lag. Humidity showed a strong immediate effect (0-3 days) of the low relative humidity at around ≤40% and a moderate effect for higher humidity (≥80%) with lag period of 0-2 days. Higher PM10 around ≥70-μg/m3 has both shorter (0-3 days) and longer lag effects (15-20 days) but the latter one is stronger comparatively. A strong effect of wind speed around ≥25 knots showed longer lag period of 8-15 days. There is a moderate effect for a shorter lag period of 0-3 days for lower wind speed (approximately 2 knots). We also notice a stronger effect of sun hours around ≥14 hours having a longer lag period of 15-20 days and moderate effect between 1-2 hours of 5-12 days lag. Similarly, higher amount of rain (≥30mm) has stronger effects, especially for the shorter lag of 0-2 days and longer lag of 7- 10 days. So far, very little research has been carried out on DLNM model in such research area and setting. This PhD research will contribute to the quantitative assessment of delayed and non-linear lag effects of climate and pollutants for the Greater London region. The methodology could easily be replicated on other disease categories and regions and not limited to LR admissions. The findings may provide useful information for the development and implementation of public health policies to reduce and prevent the impact of climate change on health problems

Asymmetrical Performance and Abnormal Synergies of the Post-Stroke Patient Wearing SCRIPT Passive Orthosis in Calibration, Exercise and Energy Evaluation

© 2014 Qin Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In the context of therapeutic human-robot interaction, it is important to detect human contribution in interaction with robots, thus to auto-tune a robot to compensate or resist based on such input. A passive orthosis is used to evaluate interaction based on kinematic data and energy flow model to identify human-contributions during interaction experiments with healthy subject and stroke patient. The results identified presence of abnormal synergies between wrist and fingers, showed a skewness apparent in stroke patient performance which seemed to decrease over-time after the rehabilitation practice and indicated lack of fine control. We hypothesise that the presented methods can be used as potential performance benchmarks allowing to identify subject’s contribution during an interaction session but also to observe extent of fine motor control over time.Peer reviewe