Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

From words to action:visibility of management in supporting interdisciplinary team working in an acute rehabilitative geriatric hospital

Purpose: The following case study aims to explore management's, health professionals' and patients' experiences on the extent to which there is visibility of management support in achieving effective interdisciplinary team working, which is explicitly declared in the mission statement of a 60-bed acute rehabilitative geriatric hospital in Malta. Design/methodology/approach: A total of 21 semi-structured interviews were conducted with the above-mentioned key stakeholders. Findings: Three main distinct yet interdependent themes emerged as a result of thematic analysis: "managing a team-friendly hospital", "interdisciplinary team components", and "interdisciplinary team processes". The findings show that visibility of management support and its alignment with the process and content levels of interdisciplinary teamwork are key to integrated care for acute rehabilitative geriatric patients. Research limitations/implications: The emerging phenomena may not be reproducible in a different context; although many of the emerging themes could be comfortably matched with the existing literature. Practical implications: The implications are geared towards raising the consciousness and conscientiousness of good practice in interdisciplinary teamwork in hospitals, as well as in emphasizing organizational and management support as crucial factors for team-based organizations. Social implications: Interdisciplinary teamwork in acute rehabilitative geriatrics provides optimal quality and integrated health care delivery with the aim that the older persons are successfully discharged back to the community. Originality/value: The authors draw on solid theoretical frameworks - the complexity theory, team effectiveness model and the social identity theory - to support their major finding, namely the alignment of organizational and management support with intra-team factors at the process and content level. © Emerald Group Publishing Limited.

Community knowledge management (CKM): a framework for community action

Knowledge management (KM) is a developing field that focuses on harnessing knowledge for use by a person or community. However, most KM research focuses on improving decision making capacity in business communities, neglecting applications in wider society and non-decision making activities. This paper explores the potential of KM for rural communities, specifically for those that want to preserve their social history and collective memories (what we call heritage) to enrich the lives of others. In KM terms, this is a task of accumulating and recording knowledge to enable its retention for future use. We report a case study of Cardrona, a valley of approximately 120 people in New Zealand’s South Island. Realising that time would erode knowledge of their community a small, motivated group of residents initiated a KM programme to create a legacy for a wider community including younger generations, tourists and scholars. This paper applies KM principles to rural communities that want to harness their collective knowledge for wider societal gain, and develops a framework to accommodate them. As a result, we call for a wider conceptualization of KM to include motives for managing knowledge beyond decision making to accommodate community KM (cKM).

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Responding to crisis through strategic knowledge management

Purpose - Many managers would like to take a strategic approach to preparing the organisation to avoid impending crisis but instead find themselves fire-fighting to mitigate its impact. This paper seeks to examine an organisation which made major strategic changes in order to respond to the full effect of a crisis which would be realised over a two to three year period. At the root of these changes was a strategic approach to managing knowledge. The paper's purpose is to reflect on managers' views of the impact this strategy had on preparing for the crisis and explore what happened in the organisation during and after the crisis. Design/methodology/approach - The paper examines a case-study of a financial services organisation which faced the crisis of its impending dissolution. The paper draws upon observations of change management workshops, as well as interviews with organisational members of a change management task force. Findings - The response to the crisis was to recognise the importance of the people and their knowledge to the organisation, and to build a strategy which improved business processes and communication flow across the divisions, as well as managing the departure of knowledge workers from an organisation in the process of being dissolved. Practical implications - The paper demonstrates the importance of building a knowledge management strategy during times of crisis, and draws out important lessons for organisations facing organisational change. Originality/value - The paper represents a unique opportunity to learn from an organisation adopting a strategic approach to managing its knowledge during a time of crisis. © Emerald Group Publishing Limited.

Actionable strategy knowledge:a practice perspective

Increasingly the body of knowledge derived from strategy theory has been criticized because it is not actionable in practice, particularly under the conditions of a knowledge economy. Since strategic management is an applied discipline this is a serious criticism. However, we argue that the theory-practice question is too simple. Accordingly, this paper expands this question by outlining first the theoretical criteria under which strategy theory is not actionable, and then outlines an alternative perspective on strategy knowledge in action, based upon a practice epistemology. The paper is in three sections. The first section explains two contextual conditions which impact upon strategy theory within a knowledge economy, environmental velocity and knowledge intensity. The impact of these contextual conditions upon the application of four different streams of strategy theory is examined. The second section suggests that the theoretical validity of these contextual conditions breaks down when we consider the knowledge artifacts, such as strategy tools and frameworks, which arise from strategy research. The third section proposes a practice epistemology for analyzing strategy knowledge in action that stands in contrast to more traditional arguments about actionable knowledge. From a practice perspective, strategy knowledge is argues to be actionable as part of the everyday activities of strategizing. © 2006 Elsevier Ltd. All rights reserved.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included.

To move or not to move, that is the question! Body schema and non-action oriented body representations:an fMRI meta-analytic study

Many studies have attempted to identify the different cognitive components of body representation (BR). Due to methodological issues, the data reported in these studies are often confusing. Here we summarize the fMRI data from previous studies and explore the possibility of a neural segregation between BR supporting actions (body-schema, BS) or not (non-oriented-to-action-body-representation, NA). We performed a general activation likelihood estimation meta-analysis of 59 fMRI experiments and two individual meta-analyses to identify the neural substrates of different BR. Body processing involves a wide network of areas in occipital, parietal, frontal and temporal lobes. NA selectively activates the somatosensory primary cortex and the supramarginal gyrus. BS involves the primary motor area and the right extrastriate body area. Our data suggest that motor information and recognition of body parts are fundamental to build BS. Instead, sensory information and processing of the egocentric perspective are more important for NA. In conclusion, our results strongly support the idea that different and segregated neural substrates are involved in body representations orient or not to actions.

AKTive Food:semantic web based knowledge conduits for the organic food industry

We present a vision and a proposal for using Semantic Web technologies in the organic food industry. This is a very knowledge intensive industry at every step from the producer, to the caterer or restauranteur, through to the consumer. There is a crucial need for a concept of environmental audit which would allow the various stake holders to know the full environmental impact of their economic choices. This is a di?erent and parallel form of knowledge to that of price. Semantic Web technologies can be used e?ectively for the calculation and transfer of this type of knowledge (together with other forms of multimedia data) which could contribute considerably to the commercial and educational impact of the organic food industry. We outline how this could be achieved as our essential ob jective is to show how advanced technologies could be used to both reduce ecological impact and increase public awareness.

Collective action and civil disobedience: the anti-GMO campaign of the Faucheurs volontaires

Civil disobedience has hitherto enjoyed only a relatively marginal place in the repertoires of French social movements, but has recently emerged as a key rallying frame for social mobilization, especially among environmental and counter-globalization movements. This paper examines the theory and practice of civil disobedience in the French context through an analysis of one such movement, the anti-GM Faucheurs Volontaires. Discussing the highly controversial campaign's positioning as 'civic disobedience', the article examines contested discourses of violence surrounding crop destruction, and the state responses to action, before asking what the campaign's claims to Republican civism mean for traditional notions of the relationship between state and challenging groups in France. It argues that framing action as civil disobedience is central to attempts to construct political and popular legitimacy, in terms of the campaign's national, international, and sectoral goals.

Inspiring lecturers: sharing e-learning practice through european case studies

What motivates a university lecturer to consider introducing a new e-learning approach to their educational practice? Accounts of e-learning practice can invite discussion and reflection on the approaches taken, reinforcement of a particular model, connection with the experience of others, vicarious learning opportunities and glimpses into tacit knowledge. If these examples provoke thinking, could they have the ‘sticky qualities’, the memorable inspiration and ideas that move us to action, when we observe the practice of others? (Szulanski, 2003) “Case studies have the capacity to inspire but also to provoke and to challenge.” (JISC, 2004) This paper will discuss a process followed for sharing best practices of e-learning. It will explain how good practices were identified and gathered by the EUNIS E-Learning Task Force collaboration, using a database and a weblog (EUNIC, 2008). It will examine the methods used for the developing and compiling of the practices and the communication of these. Actual examples of some of the case studies gathered will be included in an appendix. Suggestions of ways to develop this process further and the tangible benefits identified will be examined to ask if effective practice can also become embedded practice.

A concept-based framework for retrieving evidence to support emergency physician decision making at the point of care

The goal of evidence-based medicine is to uniformly apply evidence gained from scientific research to aspects of clinical practice. In order to achieve this goal, new applications that integrate increasingly disparate health care information resources are required. Access to and provision of evidence must be seamlessly integrated with existing clinical workflow and evidence should be made available where it is most often required - at the point of care. In this paper we address these requirements and outline a concept-based framework that captures the context of a current patient-physician encounter by combining disease and patient-specific information into a logical query mechanism for retrieving relevant evidence from the Cochrane Library. Returned documents are organized by automatically extracting concepts from the evidence-based query to create meaningful clusters of documents which are presented in a manner appropriate for point of care support. The framework is currently being implemented as a prototype software agent that operates within the larger context of a multi-agent application for supporting workflow management of emergency pediatric asthma exacerbations. © 2008 Springer-Verlag Berlin Heidelberg.

Differential impact of posterior lesions in the left and right hemisphere on visual category learning and generalization to contrast reversal

Hemispheric differences in the learning and generalization of pattern categories were explored in two experiments involving sixteen patients with unilateral posterior, cerebral lesions in the left (LH) or right (RH) hemisphere. In each experiment participants were first trained to criterion in a supervised learning paradigm to categorize a set of patterns that either consisted of simple geometric forms (Experiment 1) or unfamiliar grey-level images (Experiment 2). They were then tested for their ability to generalize acquired categorical knowledge to contrast-reversed versions of the learning patterns. The results showed that RH lesions impeded category learning of unfamiliar grey-level images more severely than LH lesions, whereas this relationship appeared reversed for categories defined by simple geometric forms. With regard to generalization to contrast reversal, categorization performance of LH and RH patients was unaffected in the case of simple geometric forms. However, generalization to of contrast-reversed grey-level images distinctly deteriorated for patients with LH lesions relative to those with RH lesions, with the latter (but not the former) being consistently unable to identify the pattern manipulation. These findings suggest a differential use of contrast information in the representation of pattern categories in the two hemispheres. Such specialization appears in line with previous distinctions between a predominantly lefthemispheric, abstract-analytical and a righthemispheric, specific-holistic representation of object categories, and their prediction of a mandatory representation of contrast polarity in the RH. Some implications for the well-established dissociation of visual disorders for the recognition of faces and letters are discussed.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.