Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Knowledge management systems:finding a way with technology

Purpose - To consider the role of technology in knowledge management in organizations, both actual and desired. Design/methodology/approach - Facilitated, computer-supported group workshops were conducted with 78 people from ten different organizations. The objective of each workshop was to review the current state of knowledge management in that organization and develop an action plan for the future. Findings - Only three organizations had adopted a strongly technology-based "solution" to knowledge management problems, and these followed three substantially different routes. There was a clear emphasis on the use of general information technology tools to support knowledge management activities, rather than the use of tools specific to knowledge management. Research limitations/implications - Further research is needed to help organizations make best use of generally available software such as intranets and e-mail for knowledge management. Many issues, especially human, relate to the implementation of any technology. Participation was restricted to organizations that wished to produce an action plan for knowledge management. The findings may therefore represent only "average" organizations, not the very best practice. Practical implications - Each organization must resolve four tensions: Between the quantity and quality of information/knowledge, between centralized and decentralized organization, between head office and organizational knowledge, and between "push" and "pull" processes. Originality/value - Although it is the group rather than an individual that determines what counts as knowledge, hardly any previous studies of knowledge management have collected data in a group context.

Scaling up question-answering to linked data

Linked Data semantic sources, in particular DBpedia, can be used to answer many user queries. PowerAqua is an open multi-ontology Question Answering (QA) system for the Semantic Web (SW). However, the emergence of Linked Data, characterized by its openness, heterogeneity and scale, introduces a new dimension to the Semantic Web scenario, in which exploiting the relevant information to extract answers for Natural Language (NL) user queries is a major challenge. In this paper we discuss the issues and lessons learned from our experience of integrating PowerAqua as a front-end for DBpedia and a subset of Linked Data sources. As such, we go one step beyond the state of the art on end-users interfaces for Linked Data by introducing mapping and fusion techniques needed to translate a user query by means of multiple sources. Our first informal experiments probe whether, in fact, it is feasible to obtain answers to user queries by composing information across semantic sources and Linked Data, even in its current form, where the strength of Linked Data is more a by-product of its size than its quality. We believe our experiences can be extrapolated to a variety of end-user applications that wish to scale, open up, exploit and re-use what possibly is the greatest wealth of data about everything in the history of Artificial Intelligence. © 2010 Springer-Verlag.

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.

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. © 2010 The authors.

Age-related macular degeneration patients' awareness of nutritional factors

Age-related macular degeneration (AMD) is the leading cause of visual impairment in older adults in the United Kingdom. This study sought to characterise AMD patients who seek the services of the Macular Society, and determine the level and source of their dietary knowledge. A questionnaire was designed, validated, and administered to 158 participants. The questions covered demographic data and knowledge of nutrition and supplementation. The mean age of participants was 79 years; 61% of them were female, and 27% were registered visually impaired. Only 55% of the participants thought diet was important for eye health, 63% felt that they had not received enough information about AMD. The participants reported that their information mainly came from non-professional support groups. Most participants identified healthy food, but could not say why, and were not able to identify carotenoid rich foods. The results of the study will inform design of education and dissemination methods regarding dietary information. © The Author(s) 2014.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

The Digital Observatory for Protected Areas (DOPA) Explorer 1.0

The Digital Observatory for Protected Areas (DOPA) has been developed to support the European Union’s efforts in strengthening our capacity to mobilize and use biodiversity data, information and forecasts so that they are readily accessible to policymakers, managers, experts and other users. Conceived as a set of web based services, DOPA provides a broad set of free and open source tools to assess, monitor and even forecast the state of and pressure on protected areas at local, regional and global scale. DOPA Explorer 1.0 is a web based interface available in four languages (EN, FR, ES, PT) providing simple means to explore the nearly 16,000 protected areas that are at least as large as 100 km2. Distinguishing between terrestrial, marine and mixed protected areas, DOPA Explorer 1.0 can help end users to identify those with most unique ecosystems and species, and assess the pressures they are exposed to because of human development. Recognized by the UN Convention on Biological Diversity (CBD) as a reference information system, DOPA Explorer is based on the best global data sets available and provides means to rank protected areas at the country and ecoregion levels. Inversely, DOPA Explorer indirectly highlights the protected areas for which information is incomplete. We finally invite the end-users of DOPA to engage with us through the proposed communication platforms to help improve our work to support the safeguarding of biodiversity.

Land resource requirements for bioenergy in India

This study presents a two stage process to determine suitable areas to grow fuel crops: i) FAO Agro Ecological Zones (AEZ) procedure is applied to four Indian states of different geographical characteristics; and ii) Modelling the growth of candidate crops with GEPIC water and nutrient model, which is used to determine potential yield of candidate crops in areas where irrigation water is brackish or soil is saline. Absence of digital soil maps, paucity of readily available climate data and knowledge of detailed requirements of candidate crops are some of the major problems, of which, a series of detailed maps will evaluate true potential of biofuels in India.

Patient experiences of serious adverse drug reactions and their attitudes to medicines:a qualitative study of survivors of Stevens-Johnson syndrome and toxic epidermal necrolysis in the UK

Background: Adverse drug reactions (ADRs) cause significant morbidity and mortality and account for around 6.5% of hospital admissions. Patient experiences of serious ADRs and their long-term impact on patients' lives, including their influence on current attitudes towards medicines, have not been previously explored. Objective: The aim of the study was to explore the experiences, beliefs, and attitudes of survivors of serious ADRs, using drug-induced Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) as a paradigm. Methods: A retrospective, qualitative study was undertaken using detailed semi-structured interviews. Fourteen adult survivors of SJS and TEN, admitted to two teaching hospitals in the UK, one the location of a tertiary burns centre, were interviewed. Interview transcripts were independently analysed by three different researchers and themes emerging from the text identified. Results: All 14 patients were aware that their condition was drug induced, and all but one knew the specific drug(s) implicated. Several expressed surprise at the perceived lack of awareness of the ADR amongst healthcare professionals, and described how the ADR was mistaken for another condition. Survivors believed that causes of the ADR included (i) being given too high a dose of the drug; (ii) medical staff ignoring existing allergies; and (iii) failure to monitor blood tests. Only two believed that the reaction was unavoidable. Those who believed that the condition could have been avoided had less trust in healthcare professionals. The ADR had a persisting impact on their current lives physically and psychologically. Many now avoided medicines altogether and were fearful of becoming ill enough to need them. © 2011 Adis Data Information BV. All rights reserved. Conclusions: Life-threatening ADRs continued to affect patients’ lives long after the event. Patients’ beliefs regarding the cause of the ADR differed, and may have influenced their trust in healthcare professionals and medicines. We propose that clear communication during the acute phase of a serious ADR may therefore be important.

Clinical significance of medication reconciliation in children admitted to a UK pediatric hospital:observational study of neurosurgical patients

Background: In December 2007, the National Institute for Health and Clinical Excellence and the National Patient Safety Agency in the UK (NICE-NPSA) published guidance that recommends all adults admitted to hospital receive medication reconciliation, usually by pharmacy staff. A costing and report tool was provided indicating a resource requirement of d12.9 million for England per year. Pediatric patients are excluded from this guidance. Objective: To determine the clinical significance of medication reconciliation in children on admission to hospital. Methods: A prospective observational study included pediatric patients admitted to a neurosurgical ward at Birmingham Childrens Hospital, Birmingham, England, between September 2006 and March 2007. Medication reconciliation was conducted by a pharmacist after the admission of each of 100 consecutive eligible patients aged 4 months to 16 years. The clinical significance of prescribing disparities between pre-admission medications and initial admission medication orders was determined by an expert multidisciplinary panel and quantified using an analog scale. The main outcome measure was the clinical signficance of unintentional variations between hospital admission medication orders and physician-prescribed pre-admission medication for repeat (continuing) medications. Results: Initial admission medication orders for children differed from prescribed pre-admission medication in 39%of cases. Half of all resulting prescribing variations in this setting had the potential to cause moderate or severe discomfort or clinical deterioration. These results mirror findings for adults. Conclusions: The introduction of medication reconciliation in children on admission to hospital has the potential to reduce discomfort or clinical deterioration by reducing unintentional changes to repeat prescribed medication. Consequently, there is no justification for the omission of children from the NICENPSA guidance concerning medication reconciliation in hospitals, and costing tools should include pediatric patients. © 2010 Adis Data Information BV. All rights reserved.

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

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.