Titlebook: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics; State-of-the-Art and Andreas Holzinger,Igor Jurisica Book 2014 S

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On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedicalthe information in large and complex data sets has been in the focus of several research fields such as statistics, data mining, machine learning, and visualization. While the first three fields predominantly rely on computational power, visualization relies mainly on human perceptual and cognitive

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A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data,on systems can facilitate the comprehension of complex scenarios and support the activities of decision makers..Unfortunately, the quality of information system archives is very poor, as widely stated by the existing literature. Data cleansing is one of the most frequently used data improvement tech

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Interactive Data Exploration Using Pattern Mining, as much insight in given data as possible. Within this field, pattern set mining aims at revealing structure in the form of sets of patterns. Although pattern set mining has shown to be an effective solution to the infamous pattern explosion, important challenges remain..One of the key challenges i

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Resources for Studying Statistical Analysis of Biomedical Data and R,ffectiveness of treatments for patients using summary statistics and to offer patients more personalized medical treatments based on predictive analytics. To exploit this opportunity, statisticians and computer scientists need to work and communicate effectively with medical practitioners to ensure

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A Kernel-Based Framework for Medical Big-Data Analytics,nalytics. The challenge typically arises from the nature of the data which may be heterogeneous, sparse, very high-dimensional, incomplete and inaccurate. Of these, standard pattern recognition methods can typically address issues of high-dimensionality, sparsity and inaccuracy. The remaining issues

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Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure,tems. However, it also has increased the number of features, and thereby the dimensionality in data, to be processed in data analysis. Higher dimensionality makes it particularly challenging to understand complex systems, by blowing up the number of possible configurations of features we need to con