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Medical research is increasingly using data from care. However, this 'secondary use' of treatment data brings with it numerous legal questions. Social and health data are among the most intimate information about a person. This legal opinion provides for the first time a detailed overview of the specific legal framework for the secondary use of medical data in the individual federal states, which also takes into account the different sponsorship and legal form of the treating institution. The analysis provides researchers and other stakeholders in the health sector with guidance in the legally compliant handling of medical treatment data. The book also contains a supplementary report, which represents the legal framework for the secondary use of the data managed via the electronic health card in accordance with Section 291a Social Code Book V.

Clinical medicine --- Research. --- Epidemiology & medical statistics --- Medicine --- clinical data --- medical research --- Big Data --- cloud4health --- Trusted Cloud

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This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Information technology industries --- machine learning --- deep learning --- image processing --- classification --- tea --- fermentation --- automated image coding --- data collection methods --- interdisciplinary learning theory --- research methods --- systematic literature review --- visitor use management --- image classification --- multi-instance learning --- divergence --- dissimilarity --- bag-to-class --- Kullback–Leibler --- segment-based temporal modeling --- two-stream network --- action recognition --- internet of things --- detection --- dataset --- plant disease recognition --- image segmentation --- aphid --- Aphoidea --- lemon --- breast cancer mammogram dataset --- ultrasound breast cancer scans --- BI-RADS --- clinical data --- machine learning --- deep learning --- image processing --- classification --- tea --- fermentation --- automated image coding --- data collection methods --- interdisciplinary learning theory --- research methods --- systematic literature review --- visitor use management --- image classification --- multi-instance learning --- divergence --- dissimilarity --- bag-to-class --- Kullback–Leibler --- segment-based temporal modeling --- two-stream network --- action recognition --- internet of things --- detection --- dataset --- plant disease recognition --- image segmentation --- aphid --- Aphoidea --- lemon --- breast cancer mammogram dataset --- ultrasound breast cancer scans --- BI-RADS --- clinical data

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The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Technology: general issues --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast

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The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Technology: general issues --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast

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Medical informatics --- Big data --- Big data. --- Medical informatics. --- Medical Informatics. --- Data Interpretation, Statistical. --- Behavioral data --- Big data for disease diagnosis --- Clinical data --- Clustering in electronic health records --- Data mining --- Data visualization --- Environmental exposure data processing --- Genetic data --- Large-scale patient data --- Medical imaging data --- Personalized care through big data --- Predictive modeling --- Security issues --- Text mining --- Unstructured clinical notes --- Data Interpretations, Statistical --- Interpretation, Statistical Data --- Statistical Data Analysis --- Statistical Data Interpretation --- Data Analysis, Statistical --- Analyses, Statistical Data --- Analysis, Statistical Data --- Data Analyses, Statistical --- Interpretations, Statistical Data --- Statistical Data Analyses --- Statistical Data Interpretations --- Computer Science, Medical --- Health Informatics --- Health Information Technology --- Informatics, Clinical --- Informatics, Medical --- Information Science, Medical --- Clinical Informatics --- Medical Computer Science --- Medical Information Science --- Health Information Technologies --- Informatics, Health --- Information Technology, Health --- Medical Computer Sciences --- Medical Information Sciences --- Science, Medical Computer --- Technology, Health Information --- Computational Biology --- Biomedical Technology --- American Recovery and Reinvestment Act --- Data sets, Large --- Large data sets --- Data sets --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Data processing --- Médecine --- Informatique