Titlebook: Machine Learning for Health Informatics; State-of-the-Art and Andreas Holzinger Book 2016 Springer International Publishing AG 2016 algorit

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Vincenzo Mancaahrtspezifische Themenbereiche wie Luftfahrtgeschichte oder Letter-Codes für Flugh?fen...Zur vertiefenden Recherche sind Verweise auf das Internet sowie empfehlenswerte Literatur angegeben. Ebenso wurden englischsprachige Begriffe aufgenommen..978-3-540-49096-8

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Machine Learning for Health Informatics,ed effort of four areas: (1)?data science, (2)?algorithms (with focus on networks and topology (structure), and entropy (time), (3)?data visualization, and last but not least (4)?privacy, data protection, safety & security.

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Bagging Soft Decision Trees,ession data sets, we show that the bagged soft trees generalize better than single soft trees and bagged hard trees. This contribution falls in the scope of research track 2 listed in the editorial, namely, machine learning algorithms.

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Empowering Bridging Term Discovery for Cross-Domain Literature Mining in the TextFlows Platform,f exploring new cross-context bridging terms. We have extended the TextFlows platform with several components, which—together with document exploration and visualization features of the CrossBee human-computer interface—make it a powerful, user-friendly text analysis tool for exploratory cross-domai

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Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records tarts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the op

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Deep Learning Trends for Focal Brain Pathology Segmentation in MRI,is chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the

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