Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models

The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK

This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnicity and social class were varied systematically. During the interviews, doctors gave free-recall accounts of their decision-making. The results do not establish that there was substantial ageism in the doctors' decisions, but rather suggest that diagnostic processes pay insufficient attention to the significance of older patients' age and its association with the likelihood of co-morbidity and atypical disease presentations. The doctors also demonstrated more limited use of ‘knowledge structures’ when diagnosing older than midlife patients. With respect to interventions, differences in the national health-care systems rather than patients' age accounted for the differences in doctors' decisions. US doctors were significantly more concerned about the potential for adverse outcomes if important diagnoses were untreated, while UK general practitioners cited greater difficulty in accessing diagnostic tests

Moderating the Influence of Current Intention to Improve Suicide Risk Prediction

When assessors evaluate a person's risk of completing suicide, the person's expressed current intention is one of the most influential factors. However, if people say they have no intention, this may not be true for a number of reasons. This paper explores the reliability of negative intention in data provided by mental-health services using the GRiST decision support system in England. It identifies features within a risk assessment record that can classify a negative statement regarding current intention of suicide as being reliable or unreliable. The algorithm is tested on previously conducted assessments, where outcomes found in later assessments do or do not match the initially stated intention. Test results show significant separation between the two classes. It means suicide predictions could be made more accurate by modifying the assessment process and associated risk judgement in accordance with a better understanding of the person's true intention

Artifact-Centric Semantic Social-Collaboration Network in an Online Healthcare Context

The emergence of Web 2.0 technology and associated social networking system, brings many possibilities and features for online collaboration. Several reference models, frameworks, tools and infrastructures have been proposed to support seamless interaction and communication between human entities in an online social environment. A few studies indicated that social networks are not only constructed on social connections of people, but also mediated by shared objects, known as object-centred sociality. However, most of the developed social software system was centred around the concept of maintaining human-centric social relationships only. This may be because of the common issues of exploiting social network approach for online collaboration related to maintaining the social behaviour, interaction and communication between human and artifacts themselves. These resources or artifacts (i.e., physical objects, software entities, documents, etc.) are active elements in a way that they may coordinate, cooperate, and even trigger collaborative work in a social environment, which is difficult understand and implement. Thus, it is essential to concentrate on exploring the artifact-centric social relations in a new generation of social-collaboration network. This paper explores the concept and characteristics of the social software system and emphasise the importance and role of objects and artifact-centric sociality in a social-collaboration network. We also outline the benefits of semantic representation of social-collaborative network structure by extending existing social ontologies such as FOAF, SIOC, and DC that define additional concepts, properties and complex social relationship of humans, social objects and collaboration artifacts. Finally, we demonstrate the effectiveness of our proposed approach by applying it to a large-scale social-collaborative healthcare service called GRaCE-AGE within the United Kingdom

From Open Content Repositories to Open Sensemaking Communities

The Open Content movement is concerned with enabling students and educators to access material, in order to then learn from it, and reuse it either in one’s studies or one’s own courses. The core efforts to date has focused on enabling access, e.g. building the organizational/political will to release and license content, and in developing open infrastructures for educators to then publish and reassemble it. The key challenge in the next phase of the open content movement is to improve the support for prospective students to engage with and learn from the material, and with each other though peer learning support, in the absence of formally imposed study timetables and assessment deadlines. This paper reports on tools for e-learning and collaborative sensemaking developed at the UK Open University which are now being considered as candidates for open content learning support

Designing multiple user perspectives and functionality for clinical decision support systems

Clinical Decision Support Systems (CDSSs) need to disseminate expertise in formats that suit different end users and with functionality tuned to the context of assessment. This paper reports research into a method for designing and implementing knowledge structures that facilitate the required flexibility. A psychological model of expertise is represented using a series of formally specified and linked XML trees that capture increasing elements of the model, starting with hierarchical structuring, incorporating reasoning with uncertainty, and ending with delivering the final CDSS. The method was applied to the Galatean Risk and Safety Tool, GRiST, which is a web-based clinical decision support system (www.egrist.org) for assessing mental-health risks. Results of its clinical implementation demonstrate that the method can produce a system that is able to deliver expertise targetted and formatted for specific patient groups, different clinical disciplines, and alternative assessment settings. The approach may be useful for developing other real-world systems using human expertise and is currently being applied to a logistics domain

Towards accessible mental healthcare through augmented reality and self-assessment tools

Mental health presents a growing public health concern worldwide with mental illnesses affecting people's quality of life and causing an economic impact on societies. The rapidly increasing demand for mental healthcare is calling for new ways of disseminating mental health knowledge and for supporting people with mental health illnesses. As an alternative to traditional mental health therapies and treatments, mental health self-assessment and self-management tools become widely available to the public. While such tools can potentially offer more timely personalised support, individuals seeking help are faced with the challenge of making an appropriate choice from an exhaustive number of online tools, mobile apps, and support programs. In this article, we present myGRaCE-a self-assessment and self-management mental health tool made accessible to users via Augmented Reality technologies. The advantage of the system is that it provides a direct pathway to relevant and reliable mental health resources and offers a positive incentive and interventions for at-risk users. To investigate the usability and intuitiveness of the system, we conducted a pilot evaluation study with 10 participants. The results showed that the majority of study participants found the system intuitive and easy to use

Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

FIELD TESTING OF REMOTE SENSOR GAS LEAK DETECTION SYSTEMS FINAL REPORT

The natural gas pipeline industry routinely checks their pipeline right-of-ways to ensure that leaks are detected. Pipeline companies use various processes to detect signs of leaking pipes, including using vehicles or low-flying aircraft. The leak detection methods range from directly sensing the gas to looking for indirect signs of leakage. The U.S. Department of Energy (DOE) and the U.S. Department of Transportation’s Office of Pipeline Safety (OPS) have provided funding to several commercial companies and research laboratories to develop advanced remote sensor systems to provide high quality, cost-effective leak detection information. To aid in the development and availability of these remote detection systems, the DOE funded a project to conduct field testing of five remote sensor leak detection systems. OPS provided co-funding for this project. The five systems chosen to be included in the field test were being developed by En’Urga Inc., ITT Industries, Inc., LaSen, Inc., Lawrence Livermore National Laboratories, and Physical Sciences Inc. The technologies included passive infrared multi-spectral scanning, laser-based differential absorption LIDAR (Light Detection and Ranging), hyperspectral imaging, and tunable diode laser absorption spectroscopy. The sensor systems were mounted in an unmodified automobile, a helicopter, or a fixed-wing aircraft. A “virtual pipeline,” that simulated conditions of an actual pipeline was created at the Rocky Mountain Oilfield Testing Center field site at NPR-3, north of Casper, Wyoming. The pipeline route was approximately 7.5 miles long and was marked by 14 direction change markers and 22 sets of road crossing markers. Fifteen leak sites, which included three types of gas releases, were established along the route, with natural gas leak rates ranging from 1 scfh to 5,000 scfh. One leak site was designated as a “calibration” site, and the location and leak rate for this site were provided to the equipment providers. Leak sites that were designed to cause plant stress were on continuously from August 30, 2004 through September 17, 2004. The remaining leak sites were set daily during the test week of September 13 to 17, 2004. Four equipment providers were scheduled to collect data along the pipeline path twice each test day. One equipment provider, at their request, was scheduled to collect data once each day for one of their platforms and twice during the entire week for their other platform. Reports of the findings for the individual equipment providers were due to Southwest Research Institute® (SwRI®) within two weeks after the testing period and are included in this report as Appendix I. Based on the data provided, leaks at many of the leak sites were successfully detected. Leak rates of 500 scfh or higher were detected at least 50% of the time. Leak rates of 100 scfh were only detected 15% of the time. Leak rates of 15 scfh and 10 scfh were only detected about 5% of the time. The 1-scfh leak was never detected. There were also a large number of “false positive” leak sites identified by the equipment providers. Some of the equipment providers made system improvements during the week including repairing malfunctioning equipment, mechanical modifications to improve performance in field applications, and developing improved data handling schemes. Other modifications have been defined for future work by some of the equipment providers. Improvements for potential future testing efforts have been identified and include improving the pipeline route and adding more leak sites