Copied Machine Learning for Health Informatics
Machine Learning for Health Informatics
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
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Immersive Systemic Knowing
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Rechnergesttzte Verfahren in Orthopdie und Unfallchirurgie
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Gas Phase Chromatography
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Reorganisationsstrategien in Wirtschaft und Verwaltung
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Deinstitutionalization and Community Living
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Cancer Epigenetics
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The Synthesis Approach to Digital System Design
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High Performance Computing in Science and Engineering 01
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God Suffers for Us
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Dynamische Grundkonstellationen in Endogenen Psychosen
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Optimization in Science and Engineering
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Immunology of Malignant Diseases
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Epistemology & Methodology III: Philosophy of Science and Technology Part I: Formal and Physical Sci
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Osteosarcoma in Adolescents and Young Adults: New Developments and Controversies
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Selective Estrogen Receptor Modulators
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Critical Risks of Different Economic Sectors
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Baader-Meinhof and the Novel
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