Titlebook: Artificial Intelligence in Medicine; 17th Conference on A David Riaño,Szymon Wilk,Annette ten Teije Conference proceedings 2019 Springer Na

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Artificial Intelligence in Medicine978-3-030-21642-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Fascism in Italian Cinema since 1945cision medicine, help detect disease before they manifest and support independent living for the elderly, amongst many other things. However, this progress will not be without challenges from both an ethical and privacy standpoint. These issues need understanding from policy makers and developers alike for AI to be embraced responsibly.

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Recent Context-Aware LSTM for Clinical Event Time-Series Predictiont occurrences for a large number of different clinical events. Our model relies on two sources of information to predict future events. One source is derived from the set of recently observed clinical events. The other one is based on the hidden state space defined by the LSTM that aims to abstract

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Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classificationn exploring their abundances in complex samples. Due to the challenges of processing a substantial amount of sequences and overall computational complexity, it is time-consuming to analyze these data through traditional database sequence comparison approaches. Deep learning has been widely used to s

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A Semi-supervised Learning Approach for Pan-Cancer Somatic Genomic Variant Classificationundreds of variants in a tumor sample. These variations should be classified as driver or passenger (i.e. benign), but functional studies could be time and cost demanding. Therefore, in the bioinformatics field, machine learning methods are widely applied to distinguish drivers from passengers. Rece

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ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identificationly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated an

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