Attribut:Beschreibung-EN

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Different visualisation methods of biosignals and image data and their characteristics and peculiarities (e.g. typical artefacts)  +
Examples of medical biosignals and filtering methods for biosignals  +
Basic principles of processing biosignal and image data by means of machine learning methods using the example of artificial neural networks (e.g. Deep Learning, Convolutional Neural Networks (CNNs), Generative Adversarial Network (GAN))  +
Application fields of image and signal processing  +
Biomedical Modelling and Simulation  +
Management of biomedical signal and image data  +
Important standards of medical informatics e.g. DICOM  +
Application scenarios for telemedical applications and their framework conditions  +
Fundamentals of data mining and data analysis of primary and secondary data sources, principles of data mining, data warehouses (data sharing), knowledge management - ideally on real conditions  +
Queries on common databases  +
Measures to ensure high data quality (based on FAIR principles)  +
Requirements for information processing in clinical studies on EDC (Electronic Data Capture) e.g. from registries (German Cancer Registry)  +
Data extraction from telemedicine and other applications  +
Ethical, political, regulatory and social aspects for dealing with big data  +
Important standards of medical informatics for data acquisition, analysis, exchange  +
Analysis methods, e.g. statistical models, "machine learning" and artificial intelligence methods  +
Different types of knowledge-based systems and medical applications of Clinical Decision Support Systems (CDSS) to optimise patient care.  +
Examples of machine learning methods and the basic principles for their evaluation using artificial neural networks as an example.  +
The concept of personalized medicine and medical applications of machine learning or artificial intelligence systems, especially in the context of medical device law  +
information logistics in health information systems  +