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Modelling clinical guidelines or protocols in a computer-executable form is a prerequisite to support their execution by Decision Support Systems. Progress of medical knowledge requires frequent updates of the encoded knowledge model. Moreover, user perception of the decision process and user preferences regarding the presentation of choices require modifications of the model. In this paper, we describe these two maintenance requirements using a protocol for the medical therapy of breast cancer and the lessons learnt in the process. The protocol was modeled in Asbru and is used at the S. Chiara Hospital in Trento. [ABSTRACT FROM AUTHOR]

Clinical practice guidelines are important instruments to support clinical care. In this work we analysed how activities are formulated in these documents and we tried to represent the activities using patterns based on semantic relations. For this we used the Unified Medical Language System (UMLS) and in particular its Semantic Network. Out of it we generated a collection of semantic patterns that can be used to automatically identify activities. In a study we showed that these semantic patterns can cover a large part of the control flow. Using such patterns cannot only support the modelling of computer-interpretable clinical practice guidelines, but can also improve the general comprehension which treatment procedures have to be accomplished. This can also lead to improved compliance of clinical practice guidelines. [ABSTRACT FROM AUTHOR]

Modelling clinical practice guidelines in a computer-interpretable format is a challenging and complex task. The modelling process involves both medical experts and computer scientists, who have to interact and communicate together. In order to support both modeller groups we propose to provide them with helpful information automatically generated using NLP methods. We identify this information using rules based on both syntactic and semantic information. The majority of the defined information extraction rules are based on semantic relationships derived from the UMLS Semantic Network. Findings in the evaluation indicate that using rules based on semantic and syntactic information provide valuable and helpful results. [ABSTRACT FROM AUTHOR]

Clinical protocols can improve the quality of care if implemented in Decision Support Systems (DSS) that are used in clinical practice. For optimal user acceptance, they must use data from the existing Electronic Patient Records (EPR) and enforce only small changes in the care process and minimal extra effort for data entry. In this paper we describe how we handle the challenge of mapping a breast cancer treatment protocol encoded in Asbru to a legacy EPR which has been used by oncologists at the point of care for years. We identified different levels of integration effort ranging from readily available data in the EPR to abstractions which can only be performed by domain experts. By involving the author of the protocol in the implementation process, we were able to design a system which promises to improve the daily routine at the places of application. [ABSTRACT FROM AUTHOR]

Especially in ill-defined problem spaces, more than one exploration way leads to a solution. But often visual analytics methods do not support the variety of problem solving strategies users might apply. Our study illustrates how knowledge on users΄ problem solving strategies can be used in the participatory design process to make a visual analytics method more flexible for different user strategies. In order to provide the users a method which functions as a real scaffold it should allow them to choose their own problem solving strategy. Therefore, an important aim for evaluation should be to test the method΄s flexibility. [ABSTRACT FROM AUTHOR]

A computerized Decision Support Systems (DSS) can improve the adherence of the clinicians to clinical guidelines and protocols. The building of a prescriptive DSS based on breast cancer treatment protocols and its integration with a legacy Electronic Patient Record is the aim of the Oncocure project. An important task of this project is the encoding of the protocols in computer-executable form – a task that requires the collaboration of physicians and computer scientists in a distributed environment. In this paper, we describe our project and how semantic wiki technology was used for the encoding task. Semantic wiki technology features great flexibility, allowing to mix unstructured information and semantic annotations, and to automatically generate the final model with minimal adaptation cost. These features render semantic wikis natural candidates for small to medium scale modeling tasks, where the adaptation and training effort of bigger systems cannot be justified. This approach is not constrained to a specific protocol modeling language, but can be used as a collaborative tool for other languages. When implemented, our DSS is expected to reduce the cost of care while improving the adherence to the guideline and the quality of the documentation. [ABSTRACT FROM AUTHOR]

Understanding novel visualizations can be a challenge even for experienced users. During iterative usability engineering phases in the DisCō project, visualizations of time-oriented data are explored by domain experts and non-experts. The aim of our study is to analyze the generation of knowledge and understanding by means of visualizations without previous user training. Focusing on applicability in various business domains for personnel planning and time scheduling, we tested mockups of visualizations with a method based on user-reported insights. Results show almost identical behavior of domain experts and non-experts when generating insights into the data from scratch. In the course of working with a visualization, an interchange of insights into the visualization and insights into the data was found. [ABSTRACT FROM AUTHOR]

Online communities have something in common: their success rise and fall with the participation rate of active users. In this paper we focus on social rewarding mechanisms that generate benefits for users in order to achieve a higher contribution rate in a wiki system. In an online community, social rewarding is in the majority of cases based on accentuation of the most active members. As money cannot be used as a motivating factor others like status, power, acceptance, and glory have to be employed. We explain different social rewarding mechanisms which aim to meet these needs of users. Furthermore, we implemented a number of methods within the MediaWiki system, where social rewarding criteria are satisfied by generating a ranking of most active members. [ABSTRACT FROM AUTHOR]

The goal of work was to determine potential of keratinolytic fungi for degradation of crude oil. Two strains belonging to species Chrysosporium keratinophilum and Trichophyton ajelloi were isolated form sewage sludge and oil waste-contaminated soil from a refinery. Preliminary experiment was performed in liquid media with increasing concentration of peptone with and without presence of hair(source of keratin). The mean value of hydrocarbons loss was 39,8% during degradation of peptone and 49,2% during degradation of peptone and hair. The ability for removal of hydrocarbons was characteristic for fungal strains and probably associated with their high degree of adaptation for living in habitats heavily contaminated with petroleum hydrocarbons. It was observed that petroleum hydrocarbon removal rates depended on fungal proteolytic activity, biomass production, and easily degradable protein content in the medium. [ABSTRACT FROM AUTHOR]

Information Extraction (IE) is an important research field within the Artificial Intelligence community, for it tries to extract relevant information out of vast amounts of data. In this paper, we propose an extraction method that utilises the content and pre-defined semantics of ontologies formulated in the Web Ontology Language (OWL) to perform the extraction task. We also propose our method to detect out-of-date constructs in the ontology to suggest changes to the user. After stating the results of our evaluation, we conclude that the use of ontologies in conjunction with IESs can indeed yield feasible results and contribute to the better scalability and portability of the system. [ABSTRACT FROM AUTHOR]

Visual Analytics is an emerging area of research and practice that aims for supporting analytical reasoning by interactive visual interfaces. The basic idea is the integration of the outstanding capabilities of humans in terms of visual information exploration and the enormous processing power of computers to form a powerful knowledge discovery environment. In the course of our half-day tutorial we will introduce this multi-disciplinary field by discussing its key issues of analytical reasoning, perception & cognition, visualization interaction, computation mining, the visual analysis process, and show potential application areas. [ABSTRACT FROM AUTHOR]

Translating clinical guidelines into formal models is beneficial in many ways, but expensive. The progress in medical knowledge requires clinical guidelines to be updated at relatively short intervals, leading to the term living guideline. This causes potentially expensive, frequent updates of the corresponding formal models. When performing these updates, there are two goals: The modelling effort must be minimised and the links between the original document and the formal model must be maintained. In this paper, we describe our solution, using tools and techniques developed during the Protocure II project. [ABSTRACT FROM AUTHOR]

Living guidelines are documents presenting up-to-date and state-of-the-art knowledge to practitioners. To have guidelines implemented by computer-support they firstly have to be formalized in a computer-interpretable form. Due to the complexity of such formats the formalization process is challenging, but burdensome and time-consuming. The LASSIE methodology supports this task by formalizing guidelines in several steps from the textual form to the guideline representation language Asbru using a document-centric approach. LASSIE uses Information Extraction technique to semi-automatically accomplish these steps. We apply LASSIE to support the implementation of living guidelines. Based on a living guideline published by the Scottish Intercollegiate Guidelines Network (SIGN) we show that adaptations of previously formalized guidelines can be accomplished easily and fast. By using this new approach only new and changed text parts have to be modeled. Furthermore, models can be inherited from previously modeled guideline versions that were added by domain experts. [ABSTRACT FROM AUTHOR]

The chapter reviews available data on the ability of keratinolytic fungi to remove hydrocarbons from different media and on the ecology of these fungi in oil-contaminated environments. [ABSTRACT FROM AUTHOR]

Clinical Practice Guidelines (CPGs) are increasingly common in clinical medicine for prescribing a set of rules that a physician should follow. Recent interest is in accurate retrieval of CPGs at the point of care. Examples are the CPGs digital libraries National Guideline Clearinghouse (NGC) or Vaidurya, which are organized along predefined concept hierarchies. In this case, both browsing and concept-based search can be applied. However, mandatory step in enabling both ways to CPGs retrieval is manual classification of CPGs along the concepts hierarchy, which is extremely time consuming. Supervised learning approaches are usually not satisfying, since commonly too few or no CPGs are provided as training set for each class. In this paper we apply for multiple classification. is an unsupervised model that supports the physician in the classification of CPGs along the concepts hierarchy, even when no labeled examples are available. This model exploits lexical and topological information on the hierarchy to elaborate a classification hypothesis for any given CPG. We argue that such a kind of unsupervised classification can support a physician to classify CPGs by recommending the most probable classes. An experimental evaluation on various concept hierarchies with hundreds of CPGs and categories provides the empirical evidence of the proposed technique. [ABSTRACT FROM AUTHOR]

In the management of chronic diseases, therapeutic decisions depend on previously administered therapies as well as patient answers to these prior treatments. To take into account the specific management of chronic diseases, the knowledge base of the guided mode of the system ASTI has been structured as a double level decision tree, a clinical level to characterize the clinical profile of a patient, and a therapeutic level to identify the new recommended treatment when taking into account the patient's therapeutic history. We propose to automatically derive the therapeutic level of the decision tree from the formal expression of guideline-based therapeutic strategies. The method has been developed using Augmented Transition Networks. Preliminary results obtained with additive therapeutic strategies such as (A, A+ B, A + B + C) where A, B, and C are therapeutic classes which can be substituted respectively by (A′, A′′), (B′, B′′), and (C′, C′′) are promising. However, the method needs to be extended to take into account more complex patterns. [ABSTRACT FROM AUTHOR]

The amount of new discoveries (as published in the scientific literature) in the area of Molecular Biology is currently growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and the extraction of the core information, for inclusion in one of the knowledge resources being maintained by the research community, becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting semantic relations (e.g. interactions between genes and proteins) from scientific literature in the domain of Molecular Biology. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus.Part of the material contained in this paper has been previously presented at the Workshop on Data Mining and Text Mining for Bioinformatics, Pisa, September 2004. [ABSTRACT FROM AUTHOR]

In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. In this paper, we present a modified IC (mIC) algorithm that uses entropy to test conditional independence and combines the steady state data with partial prior knowledge of topological ordering in gene regulatory network, for jointly learning the causal relationship among genes. We evaluate our mIC algorithm using the simulated data. The results show that the precision and recall rates are significantly improved compared with using IC algorithm. Finally, we apply the mIC algorithm to microarray data for melanoma. The algorithm identified the important causal relations associated with WNT5A, a gene playing an important role in melanoma, verified by the literatures. [ABSTRACT FROM AUTHOR]

In the paper we study the properties of cancer gene expression data sets from the perspective of classification and tumor diagnosis. Our findings and case studies are based on several recently published data sets. We find that these data sets typically include a subset of about 100 highly discriminating features of which predictive power can be further enhanced by exploring their interactions. This finding speaks against often used univariate feature selection methods, and may explain the superior performance of support vector machines recently reported in the related work. We argue that a much simpler technique that directly finds visualizations with clear separation of diagnostic classes may be used instead. Furthermore, it may perform better in inference of an understandable classifier that includes only a few relevant features. [ABSTRACT FROM AUTHOR]

Basically, one of the most important issues for identifying biological sequences is accuracy; however, since the exponential growth and excessive diversity of biological data, the requirement to compute within considerably appropriate time usually compromises with accuracy. We propose novel approaches for accurately identifying DNA sequences in shorter time by discovering sequence patterns - signatures, which are enough distinctive information for the sequence identification. The approaches are to find the best combination of n-gram patterns and six statistical scoring algorithms, which are regularly used in the research of Information Retrieval, and then employ the signatures to create a similarity scoring model for identifying the DNA. We generate two approaches to discover the signatures. For the first one, we use only statistical information extracted directly from the sequences to discover the signatures. For the second one, we use prior knowledge of the DNA in the signature discovery process. From our experiments on influenza virus, we found that: 1) our technique can identify the influenza virus at the accuracy of up to 99.69% when 11-gram is used and the prior knowledge is applied; 2) the use of too short or too long signatures produces lower efficiency; and 3) most scoring algorithms are good for identification except the "Rocchio algorithm" where its results are approximately 9% lower than the others. Moreover, this technique can be applied for identifying other organisms. [ABSTRACT FROM AUTHOR]

This paper aims at formalizing the concept of learning rules from multisource data in a cardiac monitoring context. Our method has been implemented and evaluated on learning from data describing cardiac behaviors from different viewpoints, here electrocardiograms and arterial blood pressure measures. In order to cope with the dimensionality problems of multisource learning, we propose an Inductive Logic Programming method using a two-step strategy. Firstly, rules are learned independently from each sources. Secondly, the learned rules are used to bias a new learning process from the aggregated data. The results show that the the proposed method is much more efficient than learning directly from the aggregated data. Furthermore, it yields rules having better or equal accuracy than rules obtained by monosource learning. [ABSTRACT FROM AUTHOR]

Confidence region prediction is a practically useful extension to the commonly studied pattern recognition problem. Instead of predicting a single label, the constraint is relaxed to allow prediction of a subset of labels given a desired confidence level 1 - δ. Ideally, effective region predictions should be (1) well calibrated - predictive regions at confidence level 1 - δ should err with relative frequency at most δ and (2) be as narrow (or certain) as possible. We present a simple technique to generate confidence region predictions from conditional probability estimates (probability forecasts). We use this ‘conversion' technique to generate confidence region predictions from probability forecasts output by standard machine learning algorithms when tested on 15 multi-class datasets. Our results show that approximately 44% of experiments demonstrate well-calibrated confidence region predictions, with the K-Nearest Neighbour algorithm tending to perform consistently well across all data. Our results illustrate the practical benefits of effective confidence region prediction with respect to medical diagnostics, where guarantees of capturing the true disease label can be given. [ABSTRACT FROM AUTHOR]

Automated classification systems are often used for patient diagnosis. In many cases, the rationale behind a decision is as important as the decision itself. Here we detail a method of visualizing the criteria used by a decision tree classifier to provide support for clinicians interested in diagnosing corneal disease. We leverage properties of our data transformation to create surfaces highlighting the details deemed important in classification. Preliminary results indicate that the features illustrated by our visualization method are indeed the criteria that often lead to a correct diagnosis and that our system also seems to find favor with practicing clinicians. [ABSTRACT FROM AUTHOR]