A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

The time course of plausibility effects on eye movements in reading: Evidence from noun-noun compounds.

Readers' eye movements were monitored as they read sentences containing noun-noun compounds that varied in frequency (e.g., elevator mechanic, mountain lion). The left constituent of the compound was either plausible or implausible as a head noun at the point at which it appeared, whereas the compound as a whole was always plausible. When the head noun analysis of the left constituent was implausible, reading times on this word were inflated, beginning with the first fixation. This finding is consistent with previous demonstrations of very rapid effects of plausibility on eye movements. Compound frequency did not modulate the plausibility effect, and all disruption was resolved by the time readers' eyes moved to the next word. These findings suggest (contra Kennison, 2005) that the parser initially analyzes a singular noun as a head instead of a modifier. In addition, the findings confirm that the very rapid effect of plausibility on eye movements is not due to strategic factors, because in the present experiment, unlike in previous demonstrations, this effect appeared in sentences that were globally plausible. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Working memory in mild Alzheimer's disease and early Parkinson's disease.

Alzheimer's disease (AD) and Parkinson's disease (PD) impair working memory (WM). It is unclear, however, whether the deficits seen early in the course of these diseases are similar. To address this issue, the authors compared the performance of 22 patients with mild AD, 20 patients with early PD and without dementia, and 112 control participants on tests of inhibition, short-term memory, and 2 commonly administered tests of WM. The results suggest that although mild AD and early PD both impair WM, the deficits may be related to the interruption of different processes that contribute to WM performance. Early PD disrupted inhibitory processes, whereas mild AD did not. The WM deficits seen in patients with AD may be secondary to deficits in other cognitive capacities, including semantic memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Question hard, answer simply: A comment on Storms et al. (2003).

It seems clear that, for whatever reasons, the dementia of the Alzheimer type patient group (as well as other patient groups) exhibits behavior that is different from the normal control group. G. Storms, T. Dirikx, J. Saerens, S. Verstraeten, and P. P. De Deyn (2003) rightfully argue that the observed behavior (similarity judgments) does not tell us the source (cause) of the differences between the 2 groups. Rather, the focus of the study should be placed more on finding the ways the 2 groups are different. They also point out various methodological problems in some of the previous attempts to characterize the nature of the differences. Further methodological issues in G. Storms et al.'s study are examined. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Teaching-Material Design Center: An ontology-based system for customizing reusable e-materials

Use of electronic teaching materials (e-material) to support teaching is a trend. e-Material design is therefore an important issue. Currently, most e-material providers offer a package of solutions for different purposes. However, not all teachers and learners need everything from a single package. A preferable alternative is to find useful material from different packages and combine them for a particular course. Currently, most educators collect the material manually, which is time-consuming and may result in missed material. In this paper, we describe a system – the Teaching-Material Design Center, which follows the standard of Sharable Content Object Reference Model – to separate e-material for use as teaching templates and learning objects and to label the material with use of semantic metadata for searching. This system can find existing teaching templates and learning objects for e-material designers and provide a convenient environment for constructing customized e-material for different requirements. We describe the implementation and evaluation of the proposed system for a course. Our system is efficient in finding teaching templates and learning objects and shortening the e-material development process.     

POLARIZATION BEHAVIOR OF PACKED-BED ELECTRODES: A COMPARATIVE STUDY OF EXPERIMENTAL AND MODELING RESULTS

The current distribution and overall polarization behavior of electrodeposition at a flow-through (packed bed or fluidized bed) electrode are modeled by means of a one-dimensional model involving a primary reacting species and a simultaneous side reaction. The model equations are solved by orthogonal collocation; the time and storage requirements compare favorably to those of finite-difference methods. Experimental data obtained using a packed-bed electrode are compared with the model predictions and various methods of fitting the data are compared.     

Semantic Transparency,Brain Monitoring and Evaluation of Hybrid Cognitive Architectures

In constructing hybrid systems, there is a need for a principled basis to determine the relative roles or functions of artificial neural network and symbolic approaches. The primary objective of the work to be reported is the construction of a conceptual and methodological framework that permits an iterative sequence in which a hybrid model predicts the basis of cognitive performance and an objective analysis of performance provides empirical data, evaluating (and thus constraining) the structure and processes of the model. In seeking a linkage between a hybrid model of cognition and human performance the concept of “semantic transparency” has been adopted, since it can be used in analyzing and describing both the chracteristics of a model of cognition and the processes underlying human performance. An overview of a specific, ”strong” hybrid architecture is presented. The characteristics of the virtual machines which compose it and the nature of their interaction are illustrated. An approach to the questions of evaluation is described based on empirical data obtained by brain monitoring of subjects during cognitive performance.