The clinical reasoning characteristics of diagnostic experts

The aim of this study was to identify and describe the clinical reasoning characteristics of diagnostic experts. A group of 21 experienced general practitioners were asked to complete the Diagnostic Thinking Inventory (DTI) and a set of 10 clinical reasoning problems (CRPs) to evaluate their clinical reasoning. Both the DTI and the CRPs were scored, and the CRP response patterns of each GP examined in terms of the number and type of errors contained in them. Analysis of these data showed that six GPs were able to reach the correct diagnosis using significantly less clinical information than their colleagues. These GPs also made significantly fewer interpretation errors but scored lower on both the DTI and the CRPs. Additionally, this analysis showed that more than 20% of misdiagnoses occurred despite no errors being made in the identification and interpretation of relevant clinical information. These results indicate that these six GPs diagnose efficiently, effectively and accurately using relatively few clinical data and can therefore be classified as diagnostic experts. They also indicate that a major cause of misdiagnoses is failure to properly integrate clinical data. We suggest that increased emphasis on this step in the reasoning process should prove beneficial to the development of clinical reasoning skill in undergraduate medical students.

Image fusion in GRDSS for Land use Mapping

Image fusion techniques are useful to integrate the geometric detail of a high-resolution panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MSS) image, particularly important for understanding land use dynamics at larger scale (1:25000 or lower), which is required by the decision makers to adopt holistic approaches for regional planning. Fused images can extract features from source images and provide more information than one scene of MSS image. High spectral resolution aids in identification of objects more distinctly while high spatial resolution allows locating the objects more clearly. The geoinformatics technologies with an ability to provide high-spatial-spectral-resolution data helps in inventorying, mapping, monitoring and sustainable management of natural resources. Fusion module in GRDSS, taking into consideration the limitations in spatial resolution of MSS data and spectral resolution of PAN data, provide high-spatial-spectral-resolution remote sensing images required for land use mapping on regional scale. GRDSS is a freeware GIS Graphic User Interface (GUI) developed in Tcl/Tk is based on command line arguments of GRASS (Geographic Resources Analysis Support System) with the functionalities for raster analysis, vector analysis, site analysis, image processing, modeling and graphics visualization. It has the capabilities to capture, store, process, analyse, prioritize and display spatial and temporal data.

Discovering compressing serial episodes from event sequences

Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.

Panel discussion: U.S. EPA using modeling and ecosystem services to enhance coastal decision making

This panel will discuss the research being conducted, and the models being used in three current coastal EPA studies being conducted on ecosystem services in Tampa Bay, the Chesapeake Bay and the Coastal Carolinas. These studies are intended to provide a broader and more comprehensive approach to policy and decision-making affecting coastal ecosystems as well as provide an account of valued services that have heretofore been largely unrecognized. Interim research products, including updated and integrated spatial data, models and model frameworks, and interactive decision support systems will be demonstrated to engage potential users and to elicit feedback. It is anticipated that the near-term impact of the projects will be to increase the awareness by coastal communities and coastal managers of the implications of their actions and to foster partnerships for ecosystem services research and applications. (PDF contains 4 pages)

A new adaptive marine policy toolbox to support ecosystem-based approach to management

4 p.

Metodologia para avaliação ambiental de bacias hidrográficas com uso de sistemas de suporte à decisão espacial e do indicador pegada ecológica

No atual contexto ambiental é grande a demanda por informações consistentes que subsidiem o planejamento territorial, permitindo realizar avaliações ambientais e desta forma, subsidiar os setores público e privado. Essa demanda pode ser satisfeita com a integração de informações em um sistema, com propriedades e funções de processamento, possibilitando sua utilização em ambiente integrado. Assim, nesta dissertação é proposta uma metodologia para a avaliação ambiental de bacias hidrográficas que atua desde a escolha de indicadores e definição dos pesos de sua contribuição, até a execução de avaliações e espacialização de resultados em ambiente SIG. Esta metodologia é composta por duas fases distintas: avaliação da vulnerabilidade ambiental da bacia hidrográfica a partir do uso de sistemas de suporte à decisão espacial, e, avaliação da sustentabilidade da bacia através do cálculo do indicador Pegada Ecológica. Na primeira fase são adotados sistemas de suporte à decisão, bases de conhecimento, SIG e uma ferramenta que integra estes resultados permitindo a geração de avaliações, análises e/ou cenários prospectivos. Na segunda fase, a sustentabilidade da bacia é retratada a partir do cálculo da pegada ecológica que consiste na contabilização da área que uma população necessita para produzir os recursos consumidos e absorver os resíduos gerados. A comparação entre áreas mais vulneráveis e menos sustentáveis, pode nortear projetos de recuperação e conservação ambiental.

Doação de órgãos: um estudo sobre produção de sentidos

Este trabalho teve por objetivo investigar as representações sociais construídas pelos familiares que, após a morte de seus parentes, ofereceram seus órgãos e tecidos para transplantes. Foi realizada uma pesquisa de campo utilizando como instrumento a entrevista semidirigida, também conhecida pela comunidade científica como pesquisa semiestruturada. Foram entrevistados nove familiares de doadores mortos, um doador vivo e um receptor de órgãos. Dois sentidos emergiram no estudo de campo: um ligado à ideia de vida e outro ligado à ideia de morte. No primeiro, estão as percepções da doação como cura, solidariedade, continuidade e altruísmo; no segundo ocorre principalmente a questão da fragmentação do corpo. Assim, com esta pesquisa foi possível concluir que se as pessoas em vida pudessem falar livremente aos seus familiares sobre seu desejo de serem doadoras, poderiam de certa forma facilitar aos seus familiares a decisão em doar os órgãos no difícil momento da morte. Todos os indivíduos entrevistados neste trabalho expressaram que este fator foi determinante no momento da decisão e tornou a decisão menos estressante. Refletir sobre doação de órgãos no cotidiano permite que o tema saia do anonimato e adentre tanto nas redes de apoio social quanto em instituições hospitalares e de saúde, a ponto de facilitar a decisão posterior de familiares.

Mapeamento digital do uso da terra da cobertura vegetal no Parque Estadual da Serra da Tiririca (PESET) a partir de imagens orbitais de alta resolução.

No Brasil, entre as áreas protegidas e regulamentadas por lei estão às denominadas Unidades de Conservação (UC) e são definidas assim por possuírem características ambientais, estéticas, históricas ou culturais relevantes, importantes na manutenção dos ciclos naturais, demandando regimes especiais de preservação, conservação ou exploração racional dos seus recursos. O Parque Estadual da Serra da Tiririca (PESET), criado pela Lei 1.901, de 29 de novembro de 1991 localizado entre os municípios de Niterói e Maricá no Estado do Rio de Janeiro, enquadra-se na categoria de UC de Proteção Integral abrigando uma extensa faixa de Mata Atlântica em seus limites. Para a presente pesquisa foi feita uma classificação de Uso da terra e cobertura vegetal, refinada por pesquisas feitas através do trabalho de campo, que subsidiou a elaboração da proposta de Zoneamento Ambiental para o parque. O processamento digital da imagem foi feito utilizando-se o sistema SPRING desenvolvido pelo Instituto de Pesquisas Espaciais (INPE). A confecção dos mapas temáticos foi feita com apoio do sistema Arcgis desenvolvido pela ESRI. O Sistema de Informação Geográfica (SIG) foi empregado para as modelagens ambientais. Nessa etapa foram consideradas, de forma integrada, a variabilidade taxonômica, a expressão territorial e as alterações temporais verificáveis em uma base de dados georreferenciada. A tecnologia SIG integra operações convencionais de bases de dados, relativas ao armazenamento, manipulação, análise, consulta e apresentação de dados, com possibilidades de seleção e busca de informações e suporte à análise geoestatística, conjuntamente com a possibilidade de visualização de mapas sofisticados e de análise espacial proporcionada pelos mapas. A opção por esta tecnologia busca potencializar a eficiência operacional e permitir planejamento estratégico e administração de problemas, tanto minimizando os custos operacionais como acelerando processos decisórios. O estudo feito através da modelagem computacional do PESET apresentará o emprego das técnicas amplamente utilizadas no monitoramento ambiental, sendo úteis aos profissionais destinados à gestão e aos tomadores de decisão no âmbito das políticas públicas relacionadas à gestão ambiental de Unidades de Conservação.

Project neptune: Improved operation of water distribution networks

Water service providers (WSPs) in the UK have statutory obligations to supply drinking water to all customers that complies with increasingly stringent water quality regulations and minimum flow and pressure criteria. At the same time, the industry is required by regulators and investors to demonstrate increasing operational efficiency and to meet a wide range of performance criteria that are expected to improve year-on-year. Most WSPs have an ideal for improving the operation of their water supply systems based on increased knowledge and understanding of their assets and a shift to proactive management followed by steadily increasing degrees of system monitoring, automation and optimisation. The fundamental mission is, however, to ensure security of supply, with no interruptions and water quality of the highest standard at the tap. Unfortunately, advanced technologies required to fully understand, manage and automate water supply system operation either do not yet exist, are only partially evolved, or have not yet been reliably proven for live water distribution systems. It is this deficiency that the project NEPTUNE seeks to address by carrying out research into 3 main areas; these are: data and knowledge management; pressure management (including energy management); and the associated complex decision support systems on which to base interventions. The 3-year project started in April of 2007 and has already resulted in a number of research findings under the three main research priority areas (RPA). The paper summarises in greater detail the overall project objectives, the RPA activities and the areas of research innovation that are being undertaken in this major, UK collaborative study. Copyright 2009 ASCE.

From design characteristics to system requirements

This report presents the stepwise development of requirements for a process-based design support system aimed at improving the engineering design process. The starting point was the set of characteristics identified in three sources: models of design processes in prescriptive literature; empirical studies of design in descriptive literature; and a case-study in industry. All identified characteristics and derived requirements are listed in this report.

INFORMATION VISUALIZATION TO SUPPORT MANAGEMENT DECISIONS

Information visualization can accelerate perception, provide insight and control, and harness this flood of valuable data to gain a competitive advantage in making business decisions. Although such a statement seems to be obvious, there is a lack in the literature of practical evidence of the benefit of information visualization. The main contribution of this paper is to illustrate how, for a major European apparel retailer, the visualization of performance information plays a critical role in improving business decisions and in extracting insights from Redio Frequency Idetification (RFID)-based performance measures. In this paper, we identify - based on a literature review - three fundamental managerial functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Then, we provide - based on real industrial case evidence - how information visualization supports business decision-making. Several examples are provided to evidence the benefit of information visualization through its three identified managerial functions. We find that - depending on the way performance information is shaped, communicated, and made interactive - it not only helps decision making, but also offers a means of knowledge creation, as well as an appropriate communication channel. © 2014 World Scientific Publishing Company.

Sustainability: A new imperative in contaminated land remediation

Land is not only a critical component of the earth's life support system, but also a precious resource and an important factor of production in economic systems. However, historical industrial operations have resulted in large areas of contaminated land that are only slowly being remediated. In recent years, sustainability has drawn increasing attention in the environmental remediation field. In Europe, there has been a movement towards sustainable land management; and in the US, there is an urge for green remediation. Based on a questionnaire survey and a review of existing theories and empirical evidence, this paper suggests the expanding emphasis on sustainable remediation is driven by three general factors: (1) increased recognition of secondary environmental impacts (e.g., life-cycle greenhouse gas emissions, air pollution, energy consumption, and waste production) from remediation operations, (2) stakeholders' demand for economically sustainable brownfield remediation and "green" practices, and (3) institutional pressures (e.g., social norm and public policy) that promote sustainable practices (e.g., renewable energy, green building, and waste recycling). This paper further argues that the rise of the "sustainable remediation" concept represents a critical intervention point from where the remediation field will be reshaped and new norms and standards will be established for practitioners to follow in future years. This paper presents a holistic view of sustainability considerations in remediation, and an integrated framework for sustainability assessment and decision making. The paper concludes that "sustainability" is becoming a new imperative in the environmental remediation field, with important implications for regulators, liability owners, consultants, contractors, and technology vendors. © 2014 Elsevier Ltd.

宏观经济的决策支持系统

本文介绍了一个用于宏观经济决策的决策支持系统的内容和特点。论述了处理经济系统时建立知识库系统的必要性。着重介绍了用于宏观经济的知识库系统以及建立在这个系统基础之上的整个决策支持系统的结构。最后简述了这个系统的发展方向。

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.