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.

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.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

Developing a forest data portal to support multi-scale decision making

Forests play a pivotal role in timber production, maintenance and development of biodiversity and in carbon sequestration and storage in the context of the Kyoto Protocol. Policy makers and forest experts therefore require reliable information on forest extent, type and change for management, planning and modeling purposes. It is becoming increasingly clear that such forest information is frequently inconsistent and unharmonised between countries and continents. This research paper presents a forest information portal that has been developed in line with the GEOSS and INSPIRE frameworks. The web portal provides access to forest resources data at a variety of spatial scales, from global through to regional and local, as well as providing analytical capabilities for monitoring and validating forest change. The system also allows for the utilisation of forest data and processing services within other thematic areas. The web portal has been developed using open standards to facilitate accessibility, interoperability and data transfer.

Decision theory to support evacuation in advance of catastrophic disaster including modular influence diagrams and spatial data analysis.

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Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

Az online-fogyasztói elégedettség mérésére alkalmas skála tesztelése és véglegesítése a vállalati döntéshozók támogatása céljából (Testing and finalizing a scale for measuring online consumer satisfaction to support the companies decision making process)

A versenyképesség, illetve a gazdaságos működés elengedhetetlen feltétele a fogyasztói elégedettség, melynek egyik meghatározó eleme az észlelt és elvárt minőség közti kapcsolat. A minőségi elvárások az internettel, mint napjaink egyik meghatározó csatornájával kapcsolatban is megfogalmazódtak már, így kapott jelentős szerepet az online szolgáltatásminőség meghatározása, illetve ezzel összekapcsolódva az online-fogyasztói elégedettségmérés. A tanulmány célja, hogy szakirodalmi áttekintést nyújtson a témában, és a szakirodalomból ismert E-S-QUAL és E-RecS-QUAL online-fogyasztói elégedettségmérésre szolgáló skálát megvizsgálja, érvényességét a magyar körülmények között letesztelje, és a szükségesnek látszó módosítások elvégzésével egy Magyarországon használható skálát hozzon létre. Az online-fogyasztók elégedettségmérésének alapjaként az online szolgáltatásminőség fogyasztói érzékelésével, illetve értékelésével kapcsolatos elméleteket járja körbe a tanulmány, és ezután kerül sor a különböző mérési módszerek bemutatására, kiemelt szerepet szánva az E-S-QUAL és E-RecS-QUAL skálának, mely az egyik leginkább alkalmazott módszernek számít. Az áttekintés középpontjában azok a honlapok állnak, melyeken vásárolni is lehet, a kutatást pedig az egyik jelentős hazai online könyvesbolt ügyfélkörében végeztem el. ______ Over the last decade the business-to-consumer online market has been growing very fast. In marketing literature a lot of studies have been created focusing on understanding and measuring e-service quality (e-sq) and online-customer satisfaction. The aim of the study is to summarize these concepts, analyse the relationship between e-sq and customer’s loyalty, which increases the competitiveness of the companies, and to create a valid and reliable scale to the Hungarian market for measuring online-customer satisfaction. The base of the empirical study is the E-S-QUAL and its second scale the E-RecS-QUAL that are widely used multiple scales measuring e-sq with seven dimensions: efficiency, system availability, fulfilment, privacy, responsiveness, compensation, and contact. The study is focusing on the websites customers use to shop online.