Titlebook: Artificial Intelligence in Medicine; 20th International C Martin Michalowski,Syed Sibte Raza Abidi,Samina Ab Conference proceedings 2022 Th

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https://doi.org/10.1007/978-3-319-91101-4tom checking systems based on Bayesian methods, decision trees, or information gain methods are easy to train and do not require significant computational resources. However, their drawbacks are low relevance of proposed symptoms and insufficient quality of diagnostics. The best results on these tas

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https://doi.org/10.1007/978-3-319-97199-5er, such datasets are often small, making the training of deep neural network architectures ineffective. For time-series, the suite of data augmentation tricks we can use to expand the size of the dataset is limited by the need to maintain the basic properties of the signal. Data generated by a Gene

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https://doi.org/10.1007/978-3-319-97199-5lth and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key e

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School Uniform Reforms in Modern Japano the real data sets and accelerate the innovation in the healthcare domain. Despite their promising capability, they are vulnerable to various privacy attacks that might reveal information of individuals from the training data. Preserving privacy while keeping the quality of the generated data stil