60 resultados para Case Based Computing
Resumo:
The past decade had witnessed an unprecedented growth in the amount of available digital content, and its volume is expected to continue to grow the next few years. Unstructured text data generated from web and enterprise sources form a large fraction of such content. Many of these contain large volumes of reusable data such as solutions to frequently occurring problems, and general know-how that may be reused in appropriate contexts. In this work, we address issues around leveraging unstructured text data from sources as diverse as the web and the enterprise within the Case-based Reasoning framework. Case-based Reasoning (CBR) provides a framework and methodology for systematic reuse of historical knowledge that is available in the form of problemsolution
pairs, in solving new problems. Here, we consider possibilities of enhancing Textual CBR systems under three main themes: procurement, maintenance and retrieval. We adapt and build upon the stateof-the-art techniques from data mining and natural language processing in addressing various challenges therein. Under procurement, we investigate the problem of extracting cases (i.e., problem-solution pairs) from data sources such as incident/experience
reports. We develop case-base maintenance methods specifically tuned to text targeted towards retaining solutions such that the utility of the filtered case base in solving new problems is maximized. Further, we address the problem of query suggestions for textual case-bases and show that exploiting the problem-solution partition can enhance retrieval effectiveness by prioritizing more useful query suggestions. Additionally, we illustrate interpretable clustering as a tool to drill-down to domain specific text collections (since CBR systems are usually very domain specific) and develop techniques for improved similarity assessment in social media sources such as microblogs. Through extensive empirical evaluations, we illustrate the improvements that we are able to
achieve over the state-of-the-art methods for the respective tasks.
Resumo:
AIMS AND OBJECTIVES: To explore hospice, acute care and nursing home nurses' experiences of pain management for people with advanced dementia in the final month of life. To identify the challenges, facilitators and practice areas requiring further support.
BACKGROUND: Pain management in end-stage dementia is a fundamental aspect of end of life care; however, it is unclear what challenges and facilitators nurses experience in practice, whether these differ across care settings, and whether training needs to be tailored to the context of care.
DESIGN: A qualitative study using semi-structured interviews and thematic analysis to examine data.
METHODS: 24 registered nurses caring for people dying with advanced dementia were recruited from ten nursing homes, three hospices, and two acute hospitals across a region of the United Kingdom. Interviews were conducted between June 2014 and September 2015.
RESULTS: Three core themes were identified: challenges administering analgesia, the nurse-physician relationship, and interactive learning and practice development. Patient-related challenges to pain management were universal across care settings; nurse- and organisation-related barriers differed between settings. A need for interactive learning and practice development, particularly in pharmacology, was identified.
CONCLUSIONS: Achieving pain management in practice was highly challenging. A number of barriers were identified; however, the manner and extent to which these impacted on nurses differed across hospice, nursing home and acute care settings. Needs-based training to support and promote practice development in pain management in end-stage dementia is required.
RELEVANCE TO CLINICAL PRACTICE: Nurses considered pain management fundamental to end of life care provision; however, nurses working in acute care and nursing home settings may be under-supported and under-resourced to adequately manage pain in people dying with advanced dementia. Nurse-to-nurse mentoring and ongoing needs-assessed interactive case-based learning could help promote practice development in this area. Nurses require continuing professional development in pharmacology. This article is protected by copyright. All rights reserved.
Resumo:
Background
Evidence-based practice advocates utilising best current research evidence, while reflecting patient preference and clinical expertise in decision making. Successfully incorporating this evidence into practice is a complex process. Based on recommendations of existing guidelines and systematic evidence reviews conducted using the GRADE approach, treatment pathways for common spinal pain disorders were developed.
Aims
The aim of this study was to identify important potential facilitators to the integration of these pathways into routine clinical practice.
Methods
A 22 person stakeholder group consisting of patient representatives, clinicians, researchers and members of relevant clinical interest groups took part in a series of moderated focus groups, followed up with individual, semi-structured interviews. Data were analysed using content analysis.
Results
Participants identified a number of issues which were categorized into broad themes. Common facilitators to implementation included continual education and synthesis of research evidence which is reflective of everyday practice; as well as the use of clear, unambiguous messages in recommendations. Meeting additional training needs in new or extended areas of practice was also recognized as an important factor. Different stakeholders identified specific areas which could be associated with successful uptake. Patients frequently defined early involvement in a shared decision making process as important. Clinicians identified case based examples and information on important prognostic indicators as useful tools to aiding decisions.
Conclusion
A number of potential implementation strategies were identified. Further work will examine the impact of these and other important factors on the integration of evidence-based treatment recommendations into clinical practice.
Resumo:
Wireless sensor node platforms are very diversified and very constrained, particularly in power consumption. When choosing or sizing a platform for a given application, it is necessary to be able to evaluate in an early design stage the impact of those choices. Applied to the computing platform implemented on the sensor node, it requires a good understanding of the workload it must perform. Nevertheless, this workload is highly application-dependent. It depends on the data sampling frequency together with application-specific data processing and management. It is thus necessary to have a model that can represent the workload of applications with various needs and characteristics. In this paper, we propose a workload model for wireless sensor node computing platforms. This model is based on a synthetic application that models the different computational tasks that the computing platform will perform to process sensor data. It allows to model the workload of various different applications by tuning data sampling rate and processing. A case study is performed by modeling different applications and by showing how it can be used for workload characterization. © 2011 IEEE.
Resumo:
Background: The marked increases in the incidence of type 1 diabetes in recent decades strongly suggest the role of environmental influences. These environmental influences remain largely unknown.
